{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Stock Market GAN 11-29 (s&p 500, 50k epochs, 20 history, 5 days ahead, 1 pct_change).ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "MrismZF1nVfA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Google Drive Setup"
      ]
    },
    {
      "metadata": {
        "id": "kY5SlcPumq8N",
        "colab_type": "code",
        "outputId": "60ced360-97ce-42f0-fb8a-f71d6a98590e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "#### GOOGLE DRIVE SPECIFIC ##########################\n",
        "# Make sure that you have GPU selected in the Runtime\n",
        "# If you do, will print Found GPU at: /device:GPU:0\n",
        "# Else go to Runtime -> Change Runtime Type\n",
        "\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0T-TJQGSyfs3",
        "colab_type": "code",
        "outputId": "27632b3c-6725-49ac-bd93-eac5faab85b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# CODE SNIPPET TO ACCESS THE FILES IN GOOGLE DRIVE (GO TO BROWSER AND VERIFY)\n",
        "# THEN YOU CAN ACCESS THE FILES ON LEFT SIDEBAR (copy path)\n",
        "# (https://colab.research.google.com/drive/1srw_HFWQ2SMgmWIawucXfusGzrj1_U0q#scrollTo=H4SJ-tGNkOeY)\n",
        "\n",
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "# drive.mount('/content/drive')\n",
        "drive.mount(\"/content/drive\", force_remount=True)\n",
        "\n",
        "# After executing the cell above, Drive\n",
        "# files will be present in \"/content/drive/My Drive\".\n",
        "# !ls \"/content/drive/My Drive\"\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tEgacIZfy5B7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# googlepath exists for googledrive finding the files. Can be set to empty \n",
        "# string if running on computer. Have companylist.csv in same dir. Will create\n",
        "# a stock_data folder and download stocks data into that. \n",
        "googlepath = \"drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/\"\n",
        "\n",
        "# Setting the Training Amount\n",
        "TRAINING_AMOUNT = 50000 # low to test for now\n",
        "SAVE_STEPS_AMOUNT = 10000 # testing for now\n",
        "PCT_CHANGE_AMOUNT = 5 # just want to see up down trends\n",
        "HISTORICAL_DAYS_AMOUNT = 20\n",
        "DAYS_AHEAD = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uu5MzfoPhgKv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Getting the Data\n",
        "Currently set to just download stock on first row of companylist.csv (aka AAPL) and saves it to the stock_data folder"
      ]
    },
    {
      "metadata": {
        "id": "1aIFfn9cfLyc",
        "colab_type": "code",
        "outputId": "b462fb3e-6bc5-4465-8d48-9bc6bcffeec3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8585
        }
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "Downloads stock data from alphavantage\n",
        "'''\n",
        "import pandas as pd \n",
        "import os\n",
        "import time\n",
        "import urllib\n",
        "import json\n",
        "import csv\n",
        "import requests\n",
        "import io\n",
        "from pathlib import Path\n",
        "import random\n",
        "\n",
        "ALPHA_VANTAGE_KEY = \"Redacted(get from alphavantage easily)\"\n",
        "\n",
        "'''\n",
        "Note should have companylist.csv in the directory with this file.\n",
        "'''\n",
        "\n",
        "'''\n",
        "Saves data to a file\n",
        "'''\n",
        "def save(googlepath, stock_csv, output_dir, filename):\n",
        "    try:\n",
        "        #the output dir may not exist\n",
        "        if not os.path.exists(output_dir):\n",
        "            os.makedirs(output_dir)\n",
        "    except Exception as ex:\n",
        "        print('Could not create output dir')\n",
        "        print(ex)\n",
        "        return\n",
        "    filepath = os.path.join(googlepath, output_dir, filename)\n",
        "    try:\n",
        "#         print(stock_csv)\n",
        "        df = stock_csv\n",
        "        df = df.sort_values(by='timestamp')  \n",
        "#         print(df)\n",
        "        df.to_csv(filepath, index=False)\n",
        "    except Exception as ex:\n",
        "        print('Could not open file {} to write data'.format(filepath))\n",
        "        print(ex)\n",
        "\n",
        "\n",
        "def try_download(symbol):\n",
        "    try:\n",
        "        # Keep call frequency below threshold \n",
        "        time.sleep(12)    \n",
        "        url = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={}&apikey={}&datatype=csv&outputsize=full'.format(symbol, ALPHA_VANTAGE_KEY)\n",
        "        c = pd.read_csv(url)\n",
        "        # getting rid of some columns won't look at for now\n",
        "        c = c.drop(['split_coefficient', 'dividend_amount', 'adjusted_close'], axis=1)\n",
        "        return c, True\n",
        "    except Exception as ex:\n",
        "        print(ex)\n",
        "        return None, None\n",
        "\n",
        "\n",
        "\n",
        "#Given a stock symbol (aka 'tsla') will download and save the data to the\n",
        "#output dir as a csv \n",
        "\n",
        "def download_symbol(symbol, output_dir, retry_count=4):\n",
        "\n",
        "    stock_csv, didPass = try_download(symbol)\n",
        "    if didPass:\n",
        "        save(googlepath, stock_csv, output_dir, '{}.csv'.format(symbol))\n",
        "    else:\n",
        "        print('Failed to download {}'.format(symbol))\n",
        "\n",
        "df = pd.read_csv(f\"{googlepath}companylist.csv\")\n",
        "# df = df.sort_values(by=['MarketCap'], ascending=False)\n",
        "# Top 30 Companies\n",
        "# df = df[:30]\n",
        "for symbol in df.Symbol:\n",
        "    my_file = Path(f\"{googlepath}stock_data/{symbol}.csv\")  # check if already downloaded\n",
        "#     print(my_file.exists())\n",
        "    if not my_file.exists():\n",
        "        print('Downloading {}'.format(symbol))\n",
        "        download_symbol(symbol, 'stock_data')\n",
        "    else:\n",
        "        print(f\"Already downloaded {symbol}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "Already downloaded WELL\n",
            "Already downloaded WDC\n",
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            "Already downloaded WRK\n",
            "Already downloaded WY\n",
            "Already downloaded WHR\n",
            "Already downloaded WMB\n",
            "Already downloaded WLTW\n",
            "Already downloaded WYN\n",
            "Already downloaded WYNN\n",
            "Already downloaded XEL\n",
            "Already downloaded XRX\n",
            "Already downloaded XLNX\n",
            "Already downloaded XL\n",
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            "Already downloaded YUM\n",
            "Already downloaded ZBH\n",
            "Already downloaded ZION\n",
            "Already downloaded ZTS\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "b6f-Ie82y2yf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Helper Functions to Plot: Define the Confusion Matrix"
      ]
    },
    {
      "metadata": {
        "id": "s4c-ooOFw1OJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.show()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iXdCIHYjwcs_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Initializing the GAN"
      ]
    },
    {
      "metadata": {
        "id": "Ixb_ot3CwcN2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "SEED = 42\n",
        "tf.set_random_seed(SEED)\n",
        "\n",
        "class GAN():\n",
        "\n",
        "    def sample_Z(self, batch_size, n):\n",
        "        return np.random.uniform(-1., 1., size=(batch_size, n))\n",
        "\n",
        "    def __init__(self, num_features, num_historical_days, generator_input_size=200, is_train=True):\n",
        "        def get_batch_norm_with_global_normalization_vars(size):\n",
        "            v = tf.Variable(tf.ones([size]), dtype=tf.float32)\n",
        "            m = tf.Variable(tf.ones([size]), dtype=tf.float32)\n",
        "            beta = tf.Variable(tf.ones([size]), dtype=tf.float32)\n",
        "            gamma = tf.Variable(tf.ones([size]), dtype=tf.float32)\n",
        "            return v, m, beta, gamma\n",
        "\n",
        "        self.X = tf.placeholder(tf.float32, shape=[None, num_historical_days, num_features])\n",
        "        X = tf.reshape(self.X, [-1, num_historical_days, 1, num_features])\n",
        "        self.Z = tf.placeholder(tf.float32, shape=[None, generator_input_size])\n",
        "\n",
        "        generator_output_size = num_features*num_historical_days\n",
        "        with tf.variable_scope(\"generator\"):\n",
        "            W1 = tf.Variable(tf.truncated_normal([generator_input_size, generator_output_size*10]))\n",
        "            b1 = tf.Variable(tf.truncated_normal([generator_output_size*10]))\n",
        "\n",
        "            h1 = tf.nn.sigmoid(tf.matmul(self.Z, W1) + b1)\n",
        "\n",
        "            # v1, m1, beta1, gamma1 = get_batch_norm_with_global_normalization_vars(generator_output_size*10)\n",
        "            # h1 = tf.nn.batch_norm_with_global_normalization(h1, v1, m1,\n",
        "            #         beta1, gamma1, variance_epsilon=0.000001, scale_after_normalization=False)\n",
        "\n",
        "            W2 = tf.Variable(tf.truncated_normal([generator_output_size*10, generator_output_size*5]))\n",
        "            b2 = tf.Variable(tf.truncated_normal([generator_output_size*5]))\n",
        "\n",
        "            h2 = tf.nn.sigmoid(tf.matmul(h1, W2) + b2)\n",
        "\n",
        "            # v2, m2, beta2, gamma2 = get_batch_norm_with_global_normalization_vars(generator_output_size*5)\n",
        "            # h2 = tf.nn.batch_norm_with_global_normalization(h2, v2, m2,\n",
        "            #         beta2, gamma2, variance_epsilon=0.000001, scale_after_normalization=False)\n",
        "\n",
        "\n",
        "            W3 = tf.Variable(tf.truncated_normal([generator_output_size*5, generator_output_size]))\n",
        "            b3 = tf.Variable(tf.truncated_normal([generator_output_size]))\n",
        "\n",
        "            g_log_prob = tf.matmul(h2, W3) + b3\n",
        "            g_log_prob = tf.reshape(g_log_prob, [-1, num_historical_days, 1, num_features])\n",
        "            self.gen_data = tf.reshape(g_log_prob, [-1, num_historical_days, num_features])\n",
        "            #g_log_prob = g_log_prob / tf.reshape(tf.reduce_max(g_log_prob, axis=1), [-1, 1, num_features, 1])\n",
        "            #g_prob = tf.nn.sigmoid(g_log_prob)\n",
        "\n",
        "            theta_G = [W1, b1, W2, b2, W3, b3]\n",
        "\n",
        "\n",
        "\n",
        "        with tf.variable_scope(\"discriminator\"):\n",
        "            #[filter_height, filter_width, in_channels, out_channels]\n",
        "            k1 = tf.Variable(tf.truncated_normal([3, 1, num_features, 32],\n",
        "                stddev=0.1,seed=SEED, dtype=tf.float32))\n",
        "            b1 = tf.Variable(tf.zeros([32], dtype=tf.float32))\n",
        "\n",
        "            v1, m1, beta1, gamma1 = get_batch_norm_with_global_normalization_vars(32)\n",
        "\n",
        "            k2 = tf.Variable(tf.truncated_normal([3, 1, 32, 64],\n",
        "                stddev=0.1,seed=SEED, dtype=tf.float32))\n",
        "            b2 = tf.Variable(tf.zeros([64], dtype=tf.float32))\n",
        "\n",
        "            v2, m2, beta2, gamma2 = get_batch_norm_with_global_normalization_vars(64)\n",
        "\n",
        "            k3 = tf.Variable(tf.truncated_normal([3, 1, 64, 128],\n",
        "                stddev=0.1,seed=SEED, dtype=tf.float32))\n",
        "            b3 = tf.Variable(tf.zeros([128], dtype=tf.float32))\n",
        "\n",
        "            v3, m3, beta3, gamma3 = get_batch_norm_with_global_normalization_vars(128)\n",
        "\n",
        "            W1 = tf.Variable(tf.truncated_normal([18*1*128, 128]))\n",
        "            b4 = tf.Variable(tf.truncated_normal([128]))\n",
        "\n",
        "            v4, m4, beta4, gamma4 = get_batch_norm_with_global_normalization_vars(128)\n",
        "\n",
        "            W2 = tf.Variable(tf.truncated_normal([128, 1]))\n",
        "\n",
        "            theta_D = [k1, b1, k2, b2, k3, b3, W1, b4, W2]\n",
        "\n",
        "        def discriminator(X):\n",
        "            conv = tf.nn.conv2d(X,k1,strides=[1, 1, 1, 1],padding='SAME')\n",
        "            relu = tf.nn.relu(tf.nn.bias_add(conv, b1))\n",
        "            pool = relu\n",
        "            # pool = tf.nn.avg_pool(relu, ksize=[1, 2, 1, 1], strides=[1, 2, 1, 1], padding='SAME')\n",
        "            if is_train:\n",
        "                pool = tf.nn.dropout(pool, keep_prob = 0.8)\n",
        "            # pool = tf.nn.batch_norm_with_global_normalization(pool, v1, m1,\n",
        "            #         beta1, gamma1, variance_epsilon=0.000001, scale_after_normalization=False)\n",
        "            print(pool)\n",
        "\n",
        "            conv = tf.nn.conv2d(pool, k2,strides=[1, 1, 1, 1],padding='SAME')\n",
        "            relu = tf.nn.relu(tf.nn.bias_add(conv, b2))\n",
        "            pool = relu\n",
        "            #pool = tf.nn.avg_pool(relu, ksize=[1, 2, 1, 1], strides=[1, 2, 1, 1], padding='SAME')\n",
        "            if is_train:\n",
        "                pool = tf.nn.dropout(pool, keep_prob = 0.8)\n",
        "            # pool = tf.nn.batch_norm_with_global_normalization(pool, v2, m2,\n",
        "            #         beta2, gamma2, variance_epsilon=0.000001, scale_after_normalization=False)\n",
        "            print(pool)\n",
        "\n",
        "            conv = tf.nn.conv2d(pool, k3, strides=[1, 1, 1, 1], padding='VALID')\n",
        "            relu = tf.nn.relu(tf.nn.bias_add(conv, b3))\n",
        "            if is_train:\n",
        "                relu = tf.nn.dropout(relu, keep_prob=0.8)\n",
        "            # relu = tf.nn.batch_norm_with_global_normalization(relu, v3, m3,\n",
        "            #         beta3, gamma3, variance_epsilon=0.000001, scale_after_normalization=False)\n",
        "            print(relu)\n",
        "\n",
        "\n",
        "            flattened_convolution_size = int(relu.shape[1]) * int(relu.shape[2]) * int(relu.shape[3])\n",
        "            print(flattened_convolution_size)\n",
        "            flattened_convolution = features = tf.reshape(relu, [-1, flattened_convolution_size])\n",
        "\n",
        "            if is_train:\n",
        "                flattened_convolution =  tf.nn.dropout(flattened_convolution, keep_prob=0.8)\n",
        "\n",
        "            h1 = tf.nn.relu(tf.matmul(flattened_convolution, W1) + b4)\n",
        "\n",
        "            # h1 = tf.nn.batch_norm_with_global_normalization(h1, v4, m4,\n",
        "            #         beta4, gamma4, variance_epsilon=0.000001, scale_after_normalization=False)\n",
        "\n",
        "            D_logit = tf.matmul(h1, W2)\n",
        "            D_prob = tf.nn.sigmoid(D_logit)\n",
        "            return D_prob, D_logit, features\n",
        "\n",
        "        D_real, D_logit_real, self.features = discriminator(X)\n",
        "        D_fake, D_logit_fake, _ = discriminator(g_log_prob)\n",
        "\n",
        "\n",
        "        D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))\n",
        "        D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))\n",
        "        self.D_l2_loss = (0.0001 * tf.add_n([tf.nn.l2_loss(t) for t in theta_D]) / len(theta_D))\n",
        "        self.D_loss = D_loss_real + D_loss_fake + self.D_l2_loss\n",
        "        self.G_l2_loss = (0.00001 * tf.add_n([tf.nn.l2_loss(t) for t in theta_G]) / len(theta_G))\n",
        "        self.G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake))) + self.G_l2_loss\n",
        "\n",
        "\n",
        "        self.D_solver = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(self.D_loss, var_list=theta_D)\n",
        "        self.G_solver = tf.train.AdamOptimizer(learning_rate=0.000055).minimize(self.G_loss, var_list=theta_G)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "31leQzt9wmOJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training the GAN"
      ]
    },
    {
      "metadata": {
        "id": "_N3ZkCzmwmCa",
        "colab_type": "code",
        "outputId": "fa5be6b3-67ed-4fa6-8806-345a674c6f55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17583
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "# from gan import GAN\n",
        "import random\n",
        "import tensorflow as tf\n",
        "\n",
        "random.seed(42)\n",
        "class TrainGan:\n",
        "\n",
        "    def __init__(self, num_historical_days, batch_size=128):\n",
        "        self.batch_size = batch_size\n",
        "        self.data = []\n",
        "#         files = [os.path.join('./stock_data', f) for f in os.listdir('./stock_data')]\n",
        "\n",
        "        # Google Drive Method\n",
        "        files = [f\"{googlepath}stock_data/{f}\" for f in os.listdir(f\"{googlepath}stock_data\")]\n",
        "#         print(files)\n",
        "      \n",
        "        for file in files:\n",
        "            print(file)\n",
        "            #Read in file -- note that parse_dates will be need later\n",
        "            df = pd.read_csv(file, index_col='timestamp', parse_dates=True)\n",
        "            df = df[['open','high','low','close','volume']]\n",
        "            # #Create new index with missing days\n",
        "            # idx = pd.date_range(df.index[-1], df.index[0])\n",
        "            # #Reindex and fill the missing day with the value from the day before\n",
        "            # df = df.reindex(idx, method='bfill').sort_index(ascending=False)\n",
        "            #Normilize using a of size num_historical_days\n",
        "            df = ((df -\n",
        "            df.rolling(num_historical_days).mean().shift(-num_historical_days))\n",
        "            /(df.rolling(num_historical_days).max().shift(-num_historical_days)\n",
        "            -df.rolling(num_historical_days).min().shift(-num_historical_days)))\n",
        "            #Drop the last 10 day that we don't have data for\n",
        "            df = df.dropna()\n",
        "            #Hold out the last year of trading for testing\n",
        "            #Padding to keep labels from bleeding\n",
        "            df = df[400:]\n",
        "            #This may not create good samples if num_historical_days is a\n",
        "            #mutliple of 7\n",
        "            for i in range(num_historical_days, len(df), num_historical_days):\n",
        "                self.data.append(df.values[i-num_historical_days:i])\n",
        "\n",
        "        self.gan = GAN(num_features=5, num_historical_days=num_historical_days,\n",
        "                        generator_input_size=200)\n",
        "\n",
        "    def random_batch(self, batch_size=128):\n",
        "        batch = []\n",
        "        while True:\n",
        "            batch.append(random.choice(self.data))\n",
        "            if (len(batch) == batch_size):\n",
        "                yield batch\n",
        "                batch = []\n",
        "\n",
        "    def train(self, print_steps=100, display_data=100, save_steps=SAVE_STEPS_AMOUNT):\n",
        "        if not os.path.exists(f'{googlepath}models'):\n",
        "            os.makedirs(f'{googlepath}models')\n",
        "        sess = tf.Session()\n",
        "        \n",
        "        G_loss = 0\n",
        "        D_loss = 0\n",
        "        G_l2_loss = 0\n",
        "        D_l2_loss = 0\n",
        "        sess.run(tf.global_variables_initializer())\n",
        "        saver = tf.train.Saver()\n",
        "        currentStep = \"0\"\n",
        "        \n",
        "        g_loss_array = []\n",
        "        d_loss_array = []\n",
        "        \n",
        "        if os.path.exists(f'{googlepath}models/checkpoint'):\n",
        "                with open(f'{googlepath}models/checkpoint', 'rb') as f:\n",
        "                    model_name = next(f).split('\"'.encode())[1]\n",
        "                filename = \"{}models/{}\".format(googlepath, model_name.decode())\n",
        "                currentStep = filename.split(\"-\")[1]\n",
        "                new_saver = tf.train.import_meta_graph('{}.meta'.format(filename))\n",
        "                new_saver.restore(sess, \"{}\".format(filename))\n",
        "\n",
        "        for i, X in enumerate(self.random_batch(self.batch_size)):\n",
        "\n",
        "            \n",
        "            \n",
        "            \n",
        "            if i % 1 == 0:\n",
        "                _, D_loss_curr, D_l2_loss_curr = sess.run([self.gan.D_solver, self.gan.D_loss, self.gan.D_l2_loss], feed_dict=\n",
        "                        {self.gan.X:X, self.gan.Z:self.gan.sample_Z(self.batch_size, 200)})\n",
        "                D_loss += D_loss_curr\n",
        "                D_l2_loss += D_l2_loss_curr\n",
        "            if i % 1 == 0:\n",
        "                _, G_loss_curr, G_l2_loss_curr = sess.run([self.gan.G_solver, self.gan.G_loss, self.gan.G_l2_loss],\n",
        "                        feed_dict={self.gan.Z:self.gan.sample_Z(self.batch_size, 200)})\n",
        "                G_loss += G_loss_curr\n",
        "                G_l2_loss += G_l2_loss_curr\n",
        "                \n",
        "            g_loss_array.append(G_loss_curr - G_l2_loss)\n",
        "            d_loss_array.append(D_loss_curr - D_l2_loss)\n",
        "            \n",
        "            \n",
        "            if (i+1) % print_steps == 0:\n",
        "                print('Step={} D_loss={}, G_loss={}'.format(i + int(currentStep), D_loss/print_steps - D_l2_loss/print_steps, G_loss/print_steps - G_l2_loss/print_steps))\n",
        "                #print('D_l2_loss = {} G_l2_loss={}'.format(D_l2_loss/print_steps, G_l2_loss/print_steps))\n",
        "                G_loss = 0\n",
        "                D_loss = 0\n",
        "                G_l2_loss = 0\n",
        "                D_l2_loss = 0\n",
        "            if (i+1) % save_steps == 0:\n",
        "                saver.save(sess, f'{googlepath}/models/gan.ckpt', i + int(currentStep))\n",
        "            \n",
        "            # end training at training_amount epochs\n",
        "            if ((i + int(currentStep)) > TRAINING_AMOUNT):\n",
        "                \n",
        "                print(\"Reached {} epochs for GAN\".format(i + int(currentStep)))\n",
        "                sess.close()\n",
        "                \n",
        "                axisX = np.arange(0,len(g_loss_array),1)\n",
        "                plt.plot(axisX, g_loss_array, label='generator loss')\n",
        "                plt.plot(axisX, d_loss_array, label='discriminator loss')\n",
        "                plt.legend()\n",
        "                plt.title('generator and discriminator loss')\n",
        "                plt.show()\n",
        "                \n",
        "                break\n",
        "\n",
        "            # if (i+1) % display_data == 0:\n",
        "            #     print('Generated Data')\n",
        "            #     print(sess.run(self.gan.gen_data, feed_dict={self.gan.Z:self.gan.sample_Z(1, 200)}))\n",
        "            #     print('Real Data')\n",
        "            #     print(X[0])\n",
        "\n",
        "\n",
        "# if __name__ == '__main__':\n",
        "tf.reset_default_graph()\n",
        "gan = TrainGan(HISTORICAL_DAYS_AMOUNT, 128)\n",
        "gan.train()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/MMM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AOS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ABT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ABBV.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ACN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ATVI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AYI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ADBE.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AAP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMD.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AES.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AET.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AFL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/A.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/APD.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AKAM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ARE.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALXN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALGN.csv\n",
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            "Step=42599 D_loss=0.9972618809342384, G_loss=1.1606452351808547\n",
            "Step=42699 D_loss=0.8959742936491966, G_loss=1.3335905507206918\n",
            "Step=42799 D_loss=0.7673304283618927, G_loss=1.4099750047922135\n",
            "Step=42899 D_loss=0.7624233573675155, G_loss=2.5080646935105326\n",
            "Step=42999 D_loss=1.6532557374238968, G_loss=1.582384145259857\n",
            "Step=43099 D_loss=1.262729294896126, G_loss=2.1736440467834472\n",
            "Step=43199 D_loss=1.4821905961632729, G_loss=0.7694068640470506\n",
            "Step=43299 D_loss=0.7937901118397712, G_loss=1.378607352375984\n",
            "Step=43399 D_loss=0.9172791704535483, G_loss=1.3659022226929665\n",
            "Step=43499 D_loss=0.93652653247118, G_loss=1.493530820608139\n",
            "Step=43599 D_loss=1.092846883535385, G_loss=1.3033261612057685\n",
            "Step=43699 D_loss=0.6159421384334565, G_loss=6.121413739919663\n",
            "Step=43799 D_loss=0.7988195908069611, G_loss=2.3725111442804336\n",
            "Step=43899 D_loss=1.863760133087635, G_loss=1.1518062806129457\n",
            "Step=43999 D_loss=1.1768498849868774, G_loss=0.9335637083649635\n",
            "Step=44099 D_loss=0.8416676175594329, G_loss=1.2224501234292984\n",
            "Step=44199 D_loss=1.2503907808661463, G_loss=1.5701271390914917\n",
            "Step=44299 D_loss=1.0880399045348168, G_loss=0.9712952953577041\n",
            "Step=44399 D_loss=1.3258891239762307, G_loss=1.401981898844242\n",
            "Step=44499 D_loss=1.3987942045927046, G_loss=0.987146492600441\n",
            "Step=44599 D_loss=1.1524190261960028, G_loss=1.1082694205641748\n",
            "Step=44699 D_loss=0.8014486506581306, G_loss=2.9193307435512543\n",
            "Step=44799 D_loss=0.9104661902785302, G_loss=4.029725385308266\n",
            "Step=44899 D_loss=1.284570812880993, G_loss=0.9523053979873657\n",
            "Step=44999 D_loss=1.0988709226250648, G_loss=1.2136806443333628\n",
            "Step=45099 D_loss=0.8316479241847992, G_loss=1.3590922984480858\n",
            "Step=45199 D_loss=1.2840214413404465, G_loss=1.3631402891874314\n",
            "Step=45299 D_loss=0.181858460009098, G_loss=10.087575875222683\n",
            "Step=45399 D_loss=0.5501653724908828, G_loss=4.364396143555641\n",
            "Step=45499 D_loss=1.1542494577169418, G_loss=1.1485409337282182\n",
            "Step=45599 D_loss=1.0622670552134514, G_loss=1.3268963339924813\n",
            "Step=45699 D_loss=0.8282181698083877, G_loss=1.336204579770565\n",
            "Step=45799 D_loss=1.2116378670930863, G_loss=1.103490272462368\n",
            "Step=45899 D_loss=1.3179247933626175, G_loss=1.1496509674191475\n",
            "Step=45999 D_loss=1.2831132352352141, G_loss=0.798557268679142\n",
            "Step=46099 D_loss=1.0453042936325074, G_loss=1.3069853356480599\n",
            "Step=46199 D_loss=1.1107187667489051, G_loss=1.22630267560482\n",
            "Step=46299 D_loss=0.896276145875454, G_loss=1.6299629563093188\n",
            "Step=46399 D_loss=1.1760896411538124, G_loss=1.5703264069557188\n",
            "Step=46499 D_loss=1.0191406086087227, G_loss=2.1691143414378167\n",
            "Step=46599 D_loss=1.287192626297474, G_loss=1.9227970442175863\n",
            "Step=46699 D_loss=0.9325380498170852, G_loss=1.599641573727131\n",
            "Step=46799 D_loss=0.7261772814393044, G_loss=3.100821538567543\n",
            "Step=46899 D_loss=1.2925926521420479, G_loss=1.2360592687129974\n",
            "Step=46999 D_loss=1.2561243453621864, G_loss=1.1641899940371512\n",
            "Step=47099 D_loss=0.900151869058609, G_loss=1.2589965608716012\n",
            "Step=47199 D_loss=0.9399463951587677, G_loss=2.0846752488613127\n",
            "Step=47299 D_loss=1.4125648406147957, G_loss=1.3342482602596282\n",
            "Step=47399 D_loss=0.8621345326304435, G_loss=1.6334251907467843\n",
            "Step=47499 D_loss=1.1961545971035956, G_loss=1.0727316936850546\n",
            "Step=47599 D_loss=1.0175294482707977, G_loss=1.1344306236505508\n",
            "Step=47699 D_loss=0.7368355640769004, G_loss=1.4001615902781486\n",
            "Step=47799 D_loss=0.9877293869853019, G_loss=1.288846952319145\n",
            "Step=47899 D_loss=0.9860862836241722, G_loss=1.848768619298935\n",
            "Step=47999 D_loss=0.6929764381051063, G_loss=2.2080058398842812\n",
            "Step=48099 D_loss=1.2446428114175796, G_loss=1.424928746521473\n",
            "Step=48199 D_loss=1.1928623172640802, G_loss=1.3710736641287804\n",
            "Step=48299 D_loss=1.4421904790401459, G_loss=1.2475078025460242\n",
            "Step=48399 D_loss=0.7087334856390954, G_loss=2.661362542808056\n",
            "Step=48499 D_loss=0.8712823304533959, G_loss=1.922202936112881\n",
            "Step=48599 D_loss=0.8099012541770935, G_loss=1.8057801562547682\n",
            "Step=48699 D_loss=0.9094599166512488, G_loss=2.029426960647106\n",
            "Step=48799 D_loss=1.5933349707722664, G_loss=1.5977826097607613\n",
            "Step=48899 D_loss=1.157012976706028, G_loss=1.0437917888164518\n",
            "Step=48999 D_loss=1.1365737789869308, G_loss=0.9914950144290924\n",
            "Step=49099 D_loss=0.7563211166858672, G_loss=1.4049880829453467\n",
            "Step=49199 D_loss=0.8635539123415947, G_loss=1.3549537539482117\n",
            "Step=49299 D_loss=1.5531271147727965, G_loss=2.6508281943202014\n",
            "Step=49399 D_loss=1.1272217020392419, G_loss=7.14462457627058\n",
            "Step=49499 D_loss=1.0963649743795396, G_loss=1.2359264636039735\n",
            "Step=49599 D_loss=1.1938851121068002, G_loss=1.513478399515152\n",
            "Step=49699 D_loss=1.2149623435735704, G_loss=1.2836259165406227\n",
            "Step=49799 D_loss=0.7784544920921326, G_loss=1.4908986911177635\n",
            "Step=49899 D_loss=0.8870512402057646, G_loss=3.061465363800526\n",
            "Step=49999 D_loss=1.6605734798312186, G_loss=1.170131316781044\n",
            "Reached 50001 epochs for GAN\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+2xcREdFfye6mLgnAP4a1hVIhYeyQ1j7bb8qJTJ/ti4iI6K9kF8gAYAzXYfKt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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1b7d8b40f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "KL6fVkWpx0ok"
      },
      "cell_type": "markdown",
      "source": [
        "# Initializing the CNN"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "7yV1Jr8-x0on",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "SEED = 42\n",
        "tf.set_random_seed(SEED)\n",
        "class CNN():\n",
        "\n",
        "    def __init__(self, num_features, num_historical_days, is_train=True):\n",
        "      \n",
        "        self.X = tf.placeholder(tf.float32, shape=[None, num_historical_days, num_features])\n",
        "        X = tf.reshape(self.X, [-1, num_historical_days, 1, num_features])\n",
        "        self.Y = tf.placeholder(tf.int32, shape=[None, 2])\n",
        "        self.keep_prob = tf.placeholder(tf.float32, shape=[])\n",
        "\n",
        "        with tf.variable_scope(\"cnn\"):\n",
        "            #[filter_height, filter_width, in_channels, out_channels]\n",
        "            k1 = tf.Variable(tf.truncated_normal([3, 1, num_features, 16],\n",
        "                stddev=0.1,seed=SEED, dtype=tf.float32))\n",
        "            b1 = tf.Variable(tf.zeros([16], dtype=tf.float32))\n",
        "\n",
        "            conv = tf.nn.conv2d(X,k1,strides=[1, 1, 1, 1],padding='SAME')\n",
        "            relu = tf.nn.relu(tf.nn.bias_add(conv, b1))\n",
        "            if is_train:\n",
        "                relu = tf.nn.dropout(relu, keep_prob = self.keep_prob)\n",
        "            print(relu)\n",
        "\n",
        "\n",
        "            k2 = tf.Variable(tf.truncated_normal([3, 1, 16, 32],\n",
        "                stddev=0.1,seed=SEED, dtype=tf.float32))\n",
        "            b2 = tf.Variable(tf.zeros([32], dtype=tf.float32))\n",
        "            conv = tf.nn.conv2d(relu, k2,strides=[1, 1, 1, 1],padding='SAME')\n",
        "            relu = tf.nn.relu(tf.nn.bias_add(conv, b2))\n",
        "            if is_train:\n",
        "                relu = tf.nn.dropout(relu, keep_prob = self.keep_prob)\n",
        "            print(relu)\n",
        "\n",
        "\n",
        "            k3 = tf.Variable(tf.truncated_normal([3, 1, 32, 64],\n",
        "                stddev=0.1,seed=SEED, dtype=tf.float32))\n",
        "            b3 = tf.Variable(tf.zeros([64], dtype=tf.float32))\n",
        "            conv = tf.nn.conv2d(relu, k3, strides=[1, 1, 1, 1], padding='VALID')\n",
        "            relu = tf.nn.relu(tf.nn.bias_add(conv, b3))\n",
        "            if is_train:\n",
        "                relu = tf.nn.dropout(relu, keep_prob=self.keep_prob)\n",
        "            print(relu)\n",
        "\n",
        "\n",
        "            flattened_convolution_size = int(relu.shape[1]) * int(relu.shape[2]) * int(relu.shape[3])\n",
        "            print(flattened_convolution_size)\n",
        "            flattened_convolution = features = tf.reshape(relu, [-1, flattened_convolution_size])\n",
        "\n",
        "            if is_train:\n",
        "                flattened_convolution =  tf.nn.dropout(flattened_convolution, keep_prob=self.keep_prob)\n",
        "\n",
        "            W1 = tf.Variable(tf.truncated_normal([18*1*64, 32]))\n",
        "            b4 = tf.Variable(tf.truncated_normal([32]))\n",
        "            h1 = tf.nn.relu(tf.matmul(flattened_convolution, W1) + b4)\n",
        "\n",
        "\n",
        "            W2 = tf.Variable(tf.truncated_normal([32, 2]))\n",
        "            logits = tf.matmul(h1, W2)\n",
        "\n",
        "            #self.accuracy = tf.metrics.accuracy(tf.argmax(self.Y, 1), tf.argmax(logits, 1))\n",
        "            self.accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(self.Y, 1), tf.argmax(logits, 1)), tf.float32))\n",
        "            self.confusion_matrix = tf.confusion_matrix(tf.argmax(self.Y, 1), tf.argmax(logits, 1))\n",
        "            tf.summary.scalar('accuracy', self.accuracy)\n",
        "            theta_D = [k1, b1, k2, b2, k3, b3, W1, b4, W2]           \n",
        "            \n",
        "            # D_prob = tf.nn.sigmoid(D_logit)\n",
        "\n",
        "        self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.Y, logits=logits))\n",
        "        tf.summary.scalar('loss', self.loss)\n",
        "        # self.D_l2_loss = (0.0001 * tf.add_n([tf.nn.l2_loss(t) for t in theta_D]) / len(theta_D))\n",
        "        # self.D_loss = D_loss_real + D_loss_fake + self.D_l2_loss\n",
        "        # self.G_l2_loss = (0.00001 * tf.add_n([tf.nn.l2_loss(t) for t in theta_G]) / len(theta_G))\n",
        "        # self.G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake))) + self.G_l2_loss\n",
        "\n",
        "        self.optimizer = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(self.loss)\n",
        "        self.summary = tf.summary.merge_all()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4lqp4vhRx7pz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training the CNN"
      ]
    },
    {
      "metadata": {
        "id": "j2ZFkj9Gx7cX",
        "colab_type": "code",
        "outputId": "7dcbc139-5aef-4c01-ddc8-fc3f078469ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 26654
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "# from cnn import CNN\n",
        "import random\n",
        "import tensorflow as tf\n",
        "import xgboost as xgb\n",
        "from sklearn.externals import joblib\n",
        "from sklearn.metrics import confusion_matrix\n",
        "# from plot_confusion_matrix import plot_confusion_matrix\n",
        "\n",
        "random.seed(42)\n",
        "\n",
        "class TrainCNN:\n",
        "\n",
        "    def __init__(self, num_historical_days, days=10, pct_change=0):\n",
        "        self.data = []\n",
        "        self.labels = []\n",
        "        self.test_data = []\n",
        "        self.test_labels = []\n",
        "        self.cnn = CNN(num_features=5, num_historical_days=num_historical_days, is_train=False)\n",
        "#         files = [os.path.join('./stock_data', f) for f in os.listdir('./stock_data')]\n",
        "\n",
        "        # Google Drive Method\n",
        "        files = [f\"{googlepath}stock_data/{f}\" for f in os.listdir(f\"{googlepath}stock_data\")]\n",
        "#         print(files)\n",
        "    \n",
        "    \n",
        "        for file in files:\n",
        "            print(file)\n",
        "            df = pd.read_csv(file, index_col='timestamp', parse_dates=True)\n",
        "            df = df[['open','high','low','close','volume']]\n",
        "            # data for new column labels that will use the pct_change of the closing data.\n",
        "            # pct_change measure change between current and prior element. Map these into a 1x2\n",
        "            # array to show if the pct_change > (our desired threshold) or less than.\n",
        "            labels = df.close.pct_change(days).map(lambda x: [int(x > pct_change/100.0), int(x <= pct_change/100.0)])\n",
        "            \n",
        "            # rolling normalization. (df - df.mean) / (df.max - df.min)\n",
        "            df = ((df -\n",
        "            df.rolling(num_historical_days).mean().shift(-num_historical_days))\n",
        "            /(df.rolling(num_historical_days).max().shift(-num_historical_days)\n",
        "            -df.rolling(num_historical_days).min().shift(-num_historical_days)))\n",
        "            df['labels'] = labels\n",
        "\n",
        "            # doing pct_change will give some rows (like first row) a NaN value. Drop that.\n",
        "            df = df.dropna()\n",
        "\n",
        "            # Do the testing data split\n",
        "            test_df = df[:365]\n",
        "            df = df[400:]\n",
        "\n",
        "            # get the predictors of the dataframe\n",
        "            data = df[['open','high','low','close','volume']].values\n",
        "\n",
        "            # the response value\n",
        "            labels = df['labels'].values\n",
        "\n",
        "            # start at num_historical_days and iterate the full length of the training\n",
        "            # data at intervals of num_historical_days\n",
        "            for i in range(num_historical_days, len(df), num_historical_days):\n",
        "                # split the df into arrays of length num_historical_days and append\n",
        "                # to data, i.e. array of df[curr - num_days : curr] -> a batch of values\n",
        "                self.data.append(data[i-num_historical_days:i])\n",
        "\n",
        "                # appending if price went up or down in curr day of \"i\" we are looking\n",
        "                # at\n",
        "                self.labels.append(labels[i-1])\n",
        "            \n",
        "            # do same for test data\n",
        "            data = test_df[['open','high','low','close','volume']].values\n",
        "            labels = test_df['labels'].values\n",
        "            for i in range(num_historical_days, len(test_df), 1):\n",
        "                self.test_data.append(data[i-num_historical_days:i])\n",
        "                self.test_labels.append(labels[i-1])\n",
        "\n",
        "    # a function to get a random_batch of data.\n",
        "    def random_batch(self, batch_size=128):\n",
        "        batch = []\n",
        "        labels = []\n",
        "        # zip concatenates each array index of both arrays together\n",
        "        data = list(zip(self.data, self.labels))\n",
        "        i = 0\n",
        "        while True:\n",
        "            i+= 1\n",
        "            while True:\n",
        "                # pick a random array, i.e. range of days, from data\n",
        "                d = random.choice(data)\n",
        "                # balance the data with equal number of positive pct_change\n",
        "                # and negative pct_change\n",
        "                if(d[1][0]== int(i%2)):\n",
        "                    break\n",
        "            batch.append(d[0])  # append the range of days we got to batch\n",
        "            labels.append(d[1])  # append the label of that range of data we got\n",
        "            if (len(batch) == batch_size):\n",
        "                yield batch, labels\n",
        "                batch = []\n",
        "                labels = []\n",
        "\n",
        "    def train(self, print_steps=100, display_steps=100, save_steps=SAVE_STEPS_AMOUNT, batch_size=128, keep_prob=0.6):\n",
        "        if not os.path.exists(f'{googlepath}cnn_models'):\n",
        "            os.makedirs(f'{googlepath}cnn_models')\n",
        "        if not os.path.exists(f'{googlepath}logs'):\n",
        "            os.makedirs(f'{googlepath}logs')\n",
        "        if os.path.exists(f'{googlepath}logs/train'):\n",
        "            for file in [os.path.join(f'{googlepath}logs/train/', f) for f in os.listdir(f'{googlepath}logs/train/')]:\n",
        "                os.remove(file)\n",
        "        if os.path.exists(f'{googlepath}logs/test'):\n",
        "            for file in [os.path.join(f'{googlepath}logs/test/', f) for f in os.listdir(f'{googlepath}logs/test')]:\n",
        "                os.remove(file)\n",
        "\n",
        "        sess = tf.Session()\n",
        "        loss = 0\n",
        "        l2_loss = 0\n",
        "        accuracy = 0\n",
        "        saver = tf.train.Saver()\n",
        "        train_writer = tf.summary.FileWriter(f'{googlepath}/logs/train')\n",
        "        test_writer = tf.summary.FileWriter(f'{googlepath}/logs/test')\n",
        "        sess.run(tf.global_variables_initializer())\n",
        "        \n",
        "        test_loss_array = []\n",
        "        test_accuracy_array = []\n",
        "        currentStep = \"0\"\n",
        "        \n",
        "        if os.path.exists(f'{googlepath}cnn_models/checkpoint'):\n",
        "                with open(f'{googlepath}cnn_models/checkpoint', 'rb') as f:\n",
        "                    model_name = next(f).split('\"'.encode())[1]\n",
        "                filename = \"{}cnn_models/{}\".format(googlepath, model_name.decode())\n",
        "                currentStep = filename.split(\"-\")[1]\n",
        "                new_saver = tf.train.import_meta_graph('{}.meta'.format(filename))\n",
        "                new_saver.restore(sess, \"{}\".format(filename))\n",
        "\n",
        "        for i, [X, y] in enumerate(self.random_batch(batch_size)):\n",
        "\n",
        "          \n",
        "            _, loss_curr, accuracy_curr = sess.run([self.cnn.optimizer, self.cnn.loss, self.cnn.accuracy], feed_dict=\n",
        "                    {self.cnn.X:X, self.cnn.Y:y, self.cnn.keep_prob:keep_prob})\n",
        "            loss += loss_curr\n",
        "            accuracy += accuracy_curr\n",
        "            if (i+1) % print_steps == 0:\n",
        "                print('Step={} loss={}, accuracy={}'.format(i + int(currentStep), loss/print_steps, accuracy/print_steps))\n",
        "                loss = 0\n",
        "                l2_loss = 0\n",
        "                accuracy = 0\n",
        "                test_loss, test_accuracy, confusion_matrix = sess.run([self.cnn.loss, self.cnn.accuracy, self.cnn.confusion_matrix], feed_dict={self.cnn.X:self.test_data, self.cnn.Y:self.test_labels, self.cnn.keep_prob:1})\n",
        "                test_loss_array.append(test_loss)\n",
        "                test_accuracy_array.append(test_accuracy)\n",
        "                print(\"Test loss = {}, Test accuracy = {}\".format(test_loss, test_accuracy))\n",
        "            if (i+1) % save_steps == 0:\n",
        "                saver.save(sess,  f'{googlepath}cnn_models/cnn.ckpt', i)\n",
        "\n",
        "            if (i+1) % display_steps == 0:\n",
        "                summary = sess.run(self.cnn.summary, feed_dict=\n",
        "                    {self.cnn.X:X, self.cnn.Y:y, self.cnn.keep_prob:keep_prob})\n",
        "                train_writer.add_summary(summary, i)\n",
        "                summary = sess.run(self.cnn.summary, feed_dict={\n",
        "                    self.cnn.X:self.test_data, self.cnn.Y:self.test_labels, self.cnn.keep_prob:1})\n",
        "                test_writer.add_summary(summary, i)\n",
        "            \n",
        "            # end training at training_amount epochs\n",
        "            if (i + int(currentStep)) > TRAINING_AMOUNT:\n",
        "                print(\"Reached {} epochs for CNN\".format(i + int(currentStep)))\n",
        "                sess.close()\n",
        "                print(confusion_matrix)\n",
        "                plot_confusion_matrix(confusion_matrix, ['Down', 'Up'], normalize=True, title=\"CNN Confusion Matrix\")\n",
        "                \n",
        "                axisX = np.arange(0,len(test_loss_array),1)\n",
        "                plt.plot(axisX, test_loss_array, label='test loss')\n",
        "                plt.plot(axisX, test_accuracy_array, label='test accuracy')\n",
        "                plt.legend()\n",
        "                plt.title('test loss and accuracy')\n",
        "                plt.show()\n",
        "\n",
        "                break\n",
        "\n",
        "\n",
        "# if __name__ == '__main__':\n",
        "tf.reset_default_graph()\n",
        "cnn = TrainCNN(num_historical_days=HISTORICAL_DAYS_AMOUNT, days=DAYS_AHEAD, pct_change=PCT_CHANGE_AMOUNT)\n",
        "cnn.train()\n",
        "# print(confusion_matrix)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"cnn/Relu:0\", shape=(?, 20, 1, 16), dtype=float32)\n",
            "Tensor(\"cnn/Relu_1:0\", shape=(?, 20, 1, 32), dtype=float32)\n",
            "Tensor(\"cnn/Relu_2:0\", shape=(?, 18, 1, 64), dtype=float32)\n",
            "1152\n",
            "WARNING:tensorflow:From <ipython-input-8-3ac5478d33d2>:73: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
            "\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/MMM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AOS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ABT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ABBV.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ACN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ATVI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AYI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ADBE.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AAP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMD.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AES.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AET.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AFL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/A.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/APD.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AKAM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ARE.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALXN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALGN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALLE.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AGN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ADS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/LNT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ALL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/GOOGL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/GOOG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/MO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMZN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AEE.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AAL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AEP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AXP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AIG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AWK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ABC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AME.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMGN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/APH.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/APC.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/APA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AIV.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AAPL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/AMAT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/APTV.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ADM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ARNC.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/T.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ADSK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ADP.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CHRW.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/KMX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CCL.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CNC.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CTL.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CF.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CHTR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CVX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CMG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CHD.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XEC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CINF.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CTAS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CSCO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/C.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CFG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CTXS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CME.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CMS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/KO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CTSH.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CMCSA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CMA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CAG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CXO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/COP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ED.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/STZ.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/GLW.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/RTN.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/RHI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ROK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/COL.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SPG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SWKS.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/LUV.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SBUX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/STT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SRCL.csv\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/STI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SIVB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SYMC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SYF.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SNPS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/SYY.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TROW.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TTWO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TPR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TGT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TEL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/FTI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TXN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TXT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/BK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/CLX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/COO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/HSY.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/MOS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TRV.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/DIS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TMO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TIF.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TWX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TJX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TMK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TSS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TSCO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TDG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TRIP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/FOXA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/FOX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TSN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/USB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UDR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ULTA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UAA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UNP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UAL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UNH.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UPS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/URI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UTX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UHS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UNM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VFC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VLO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VAR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VTR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VRSN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VRSK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VZ.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VRTX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VIAB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/V.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VNO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VMC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WMT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WBA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WAT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WEC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WFC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WELL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WDC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WU.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WRK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WY.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WHR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WMB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WLTW.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WYN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WYNN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XEL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XRX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XLNX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XYL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/YUM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZBH.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZION.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZTS.csv\n",
            "Step=99 loss=1.2480168133974074, accuracy=0.59765625\n",
            "Test loss = 0.6358456611633301, Test accuracy = 0.7113056182861328\n",
            "Step=199 loss=0.5621399334073067, accuracy=0.74203125\n",
            "Test loss = 0.4963250458240509, Test accuracy = 0.769865095615387\n",
            "Step=299 loss=0.4924930667877197, accuracy=0.767890625\n",
            "Test loss = 0.43283364176750183, Test accuracy = 0.8023718595504761\n",
            "Step=399 loss=0.44809012234210965, accuracy=0.792734375\n",
            "Test loss = 0.4053196907043457, Test accuracy = 0.818017303943634\n",
            "Step=499 loss=0.41783235281705855, accuracy=0.811328125\n",
            "Test loss = 0.38983848690986633, Test accuracy = 0.8257507085800171\n",
            "Step=599 loss=0.4092918160557747, accuracy=0.817578125\n",
            "Test loss = 0.36866921186447144, Test accuracy = 0.8392179012298584\n",
            "Step=699 loss=0.3963255250453949, accuracy=0.826796875\n",
            "Test loss = 0.35706663131713867, Test accuracy = 0.8442428708076477\n",
            "Step=799 loss=0.3840140601992607, accuracy=0.82453125\n",
            "Test loss = 0.37401264905929565, Test accuracy = 0.8364403247833252\n",
            "Step=899 loss=0.36812755167484285, accuracy=0.834453125\n",
            "Test loss = 0.3629208505153656, Test accuracy = 0.8412175178527832\n",
            "Step=999 loss=0.3728048065304756, accuracy=0.841953125\n",
            "Test loss = 0.35383448004722595, Test accuracy = 0.8439950942993164\n",
            "Step=1099 loss=0.3634080168604851, accuracy=0.838359375\n",
            "Test loss = 0.3374396860599518, Test accuracy = 0.8525813817977905\n",
            "Step=1199 loss=0.3605558243393898, accuracy=0.841484375\n",
            "Test loss = 0.3305458724498749, Test accuracy = 0.8547480702400208\n",
            "Step=1299 loss=0.3491046383976936, accuracy=0.84390625\n",
            "Test loss = 0.32671988010406494, Test accuracy = 0.8562579154968262\n",
            "Step=1399 loss=0.351566755771637, accuracy=0.84796875\n",
            "Test loss = 0.32251372933387756, Test accuracy = 0.8581480383872986\n",
            "Step=1499 loss=0.35570510193705557, accuracy=0.843046875\n",
            "Test loss = 0.32193517684936523, Test accuracy = 0.8575314283370972\n",
            "Step=1599 loss=0.359334207624197, accuracy=0.849765625\n",
            "Test loss = 0.3096602261066437, Test accuracy = 0.8621529936790466\n",
            "Step=1699 loss=0.3412898690998554, accuracy=0.8528125\n",
            "Test loss = 0.3275589942932129, Test accuracy = 0.8535264134407043\n",
            "Step=1799 loss=0.3390994356572628, accuracy=0.850390625\n",
            "Test loss = 0.3052918016910553, Test accuracy = 0.8649939894676208\n",
            "Step=1899 loss=0.34352562576532364, accuracy=0.852265625\n",
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            "Step=1999 loss=0.3325162824988365, accuracy=0.855234375\n",
            "Test loss = 0.3085326552391052, Test accuracy = 0.8624008297920227\n",
            "Step=2099 loss=0.3301545880734921, accuracy=0.859609375\n",
            "Test loss = 0.31933754682540894, Test accuracy = 0.8573009371757507\n",
            "Step=2199 loss=0.33991727635264396, accuracy=0.85\n",
            "Test loss = 0.2938787341117859, Test accuracy = 0.8693619966506958\n",
            "Step=2299 loss=0.3291897137463093, accuracy=0.853359375\n",
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            "Step=2399 loss=0.3370418730378151, accuracy=0.854375\n",
            "Test loss = 0.285299688577652, Test accuracy = 0.8728829622268677\n",
            "Step=2499 loss=0.3271533703804016, accuracy=0.8565625\n",
            "Test loss = 0.2851678729057312, Test accuracy = 0.8728138208389282\n",
            "Step=2599 loss=0.33132401034235953, accuracy=0.85578125\n",
            "Test loss = 0.31012800335884094, Test accuracy = 0.8600957989692688\n",
            "Step=2699 loss=0.32073941841721537, accuracy=0.859765625\n",
            "Test loss = 0.29000332951545715, Test accuracy = 0.8703531622886658\n",
            "Step=2799 loss=0.3093400894105434, accuracy=0.870546875\n",
            "Test loss = 0.2941603362560272, Test accuracy = 0.8680769801139832\n",
            "Step=2899 loss=0.31972563490271566, accuracy=0.862578125\n",
            "Test loss = 0.28233250975608826, Test accuracy = 0.8736205697059631\n",
            "Step=2999 loss=0.327501600086689, accuracy=0.858359375\n",
            "Test loss = 0.2900093197822571, Test accuracy = 0.8694196343421936\n",
            "Step=3099 loss=0.31306403905153274, accuracy=0.864453125\n",
            "Test loss = 0.2792399227619171, Test accuracy = 0.8749920725822449\n",
            "Step=3199 loss=0.31306609004735947, accuracy=0.86484375\n",
            "Test loss = 0.30234190821647644, Test accuracy = 0.8632363677024841\n",
            "Step=3299 loss=0.3076058292388916, accuracy=0.868046875\n",
            "Test loss = 0.2813054323196411, Test accuracy = 0.8738453388214111\n",
            "Step=3399 loss=0.31683428764343263, accuracy=0.861484375\n",
            "Test loss = 0.2647393047809601, Test accuracy = 0.8818841576576233\n",
            "Step=3499 loss=0.3133312402665615, accuracy=0.861953125\n",
            "Test loss = 0.2998245656490326, Test accuracy = 0.863760769367218\n",
            "Step=3599 loss=0.3130137442052364, accuracy=0.863515625\n",
            "Test loss = 0.28284794092178345, Test accuracy = 0.8718053698539734\n",
            "Step=3699 loss=0.30701813265681266, accuracy=0.869140625\n",
            "Test loss = 0.27468788623809814, Test accuracy = 0.8759832382202148\n",
            "Step=3799 loss=0.312427167147398, accuracy=0.8646875\n",
            "Test loss = 0.28027117252349854, Test accuracy = 0.8730846643447876\n",
            "Step=3899 loss=0.30817524835467336, accuracy=0.86453125\n",
            "Test loss = 0.2863079607486725, Test accuracy = 0.8702379465103149\n",
            "Step=3999 loss=0.31104585334658624, accuracy=0.865546875\n",
            "Test loss = 0.27165645360946655, Test accuracy = 0.8769456148147583\n",
            "Step=4099 loss=0.30114470928907394, accuracy=0.869140625\n",
            "Test loss = 0.29531607031822205, Test accuracy = 0.8659793734550476\n",
            "Step=4199 loss=0.3094248367846012, accuracy=0.868984375\n",
            "Test loss = 0.28079894185066223, Test accuracy = 0.8723354935646057\n",
            "Step=4299 loss=0.306220293790102, accuracy=0.868671875\n",
            "Test loss = 0.2853895425796509, Test accuracy = 0.8709467649459839\n",
            "Step=4399 loss=0.30245998308062555, accuracy=0.867890625\n",
            "Test loss = 0.28510376811027527, Test accuracy = 0.8712982535362244\n",
            "Step=4499 loss=0.3000859174132347, accuracy=0.871171875\n",
            "Test loss = 0.2741978168487549, Test accuracy = 0.8756547570228577\n",
            "Step=4599 loss=0.30664643287658694, accuracy=0.866953125\n",
            "Test loss = 0.26068785786628723, Test accuracy = 0.8820455074310303\n",
            "Step=4699 loss=0.2958487591147423, accuracy=0.8765625\n",
            "Test loss = 0.27774205803871155, Test accuracy = 0.8736551403999329\n",
            "Step=4799 loss=0.2977441550791264, accuracy=0.873984375\n",
            "Test loss = 0.2773490250110626, Test accuracy = 0.8731999397277832\n",
            "Step=4899 loss=0.30927428930997847, accuracy=0.876640625\n",
            "Test loss = 0.2836465537548065, Test accuracy = 0.8704684376716614\n",
            "Step=4999 loss=0.30021578297019, accuracy=0.86953125\n",
            "Test loss = 0.26976844668388367, Test accuracy = 0.8821261525154114\n",
            "Step=5099 loss=0.30156132087111476, accuracy=0.871328125\n",
            "Test loss = 0.2808176279067993, Test accuracy = 0.875643253326416\n",
            "Step=5199 loss=0.28558951497077945, accuracy=0.882890625\n",
            "Test loss = 0.2786424160003662, Test accuracy = 0.8762713670730591\n",
            "Step=5299 loss=0.284615658223629, accuracy=0.8825\n",
            "Test loss = 0.28029096126556396, Test accuracy = 0.8752398490905762\n",
            "Step=5399 loss=0.29326688319444655, accuracy=0.876640625\n",
            "Test loss = 0.28354260325431824, Test accuracy = 0.8737877011299133\n",
            "Step=5499 loss=0.2977044986188412, accuracy=0.871875\n",
            "Test loss = 0.2562684714794159, Test accuracy = 0.8864135146141052\n",
            "Step=5599 loss=0.29538464426994326, accuracy=0.872265625\n",
            "Test loss = 0.2842355966567993, Test accuracy = 0.8722375631332397\n",
            "Step=5699 loss=0.2949134396016598, accuracy=0.87734375\n",
            "Test loss = 0.2647837996482849, Test accuracy = 0.8813194036483765\n",
            "Step=5799 loss=0.29782901376485826, accuracy=0.872265625\n",
            "Test loss = 0.294604629278183, Test accuracy = 0.8672817349433899\n",
            "Step=5899 loss=0.2891628165543079, accuracy=0.877578125\n",
            "Test loss = 0.25966864824295044, Test accuracy = 0.8841315507888794\n",
            "Step=5999 loss=0.28709383219480517, accuracy=0.8784375\n",
            "Test loss = 0.2682419717311859, Test accuracy = 0.8800170421600342\n",
            "Step=6099 loss=0.28766418784856795, accuracy=0.875078125\n",
            "Test loss = 0.2659986913204193, Test accuracy = 0.8805414438247681\n",
            "Step=6199 loss=0.28496783241629603, accuracy=0.876953125\n",
            "Test loss = 0.2681686580181122, Test accuracy = 0.8793543577194214\n",
            "Step=6299 loss=0.28440332621335984, accuracy=0.88015625\n",
            "Test loss = 0.25690045952796936, Test accuracy = 0.8850074410438538\n",
            "Step=6399 loss=0.28852730095386503, accuracy=0.878359375\n",
            "Test loss = 0.2822389602661133, Test accuracy = 0.8722721338272095\n",
            "Step=6499 loss=0.29231524750590326, accuracy=0.876328125\n",
            "Test loss = 0.2606884241104126, Test accuracy = 0.8827542662620544\n",
            "Step=6599 loss=0.28588625609874724, accuracy=0.876640625\n",
            "Test loss = 0.2755381762981415, Test accuracy = 0.876288652420044\n",
            "Step=6699 loss=0.2926778906583786, accuracy=0.874140625\n",
            "Test loss = 0.25278240442276, Test accuracy = 0.8868054151535034\n",
            "Step=6799 loss=0.28556168243288993, accuracy=0.8790625\n",
            "Test loss = 0.2593841254711151, Test accuracy = 0.8828753232955933\n",
            "Step=6899 loss=0.2860090711712837, accuracy=0.8765625\n",
            "Test loss = 0.2675550580024719, Test accuracy = 0.8785303235054016\n",
            "Step=6999 loss=0.2780692879855633, accuracy=0.881015625\n",
            "Test loss = 0.25112590193748474, Test accuracy = 0.8867074251174927\n",
            "Step=7099 loss=0.2832610258460045, accuracy=0.88078125\n",
            "Test loss = 0.2615621089935303, Test accuracy = 0.881855309009552\n",
            "Step=7199 loss=0.2850514842569828, accuracy=0.87875\n",
            "Test loss = 0.25319916009902954, Test accuracy = 0.8855952620506287\n",
            "Step=7299 loss=0.2738447016477585, accuracy=0.882890625\n",
            "Test loss = 0.25067174434661865, Test accuracy = 0.8870762586593628\n",
            "Step=7399 loss=0.27841985478997233, accuracy=0.88265625\n",
            "Test loss = 0.2472693920135498, Test accuracy = 0.8887012600898743\n",
            "Step=7499 loss=0.28486927539110185, accuracy=0.8790625\n",
            "Test loss = 0.2647378146648407, Test accuracy = 0.8814288973808289\n",
            "Step=7599 loss=0.2845985545217991, accuracy=0.87953125\n",
            "Test loss = 0.2626016139984131, Test accuracy = 0.881693959236145\n",
            "Step=7699 loss=0.28024749740958216, accuracy=0.884921875\n",
            "Test loss = 0.25061628222465515, Test accuracy = 0.886955201625824\n",
            "Step=7799 loss=0.2785797065496445, accuracy=0.8796875\n",
            "Test loss = 0.26897287368774414, Test accuracy = 0.8779770731925964\n",
            "Step=7899 loss=0.27655348628759385, accuracy=0.8825\n",
            "Test loss = 0.2620568573474884, Test accuracy = 0.8814404010772705\n",
            "Step=7999 loss=0.2754978157579899, accuracy=0.88375\n",
            "Test loss = 0.26026788353919983, Test accuracy = 0.8822356462478638\n",
            "Step=8099 loss=0.27962759912014007, accuracy=0.88265625\n",
            "Test loss = 0.26323363184928894, Test accuracy = 0.8804838061332703\n",
            "Step=8199 loss=0.2772576563060284, accuracy=0.882421875\n",
            "Test loss = 0.2742791473865509, Test accuracy = 0.8753032684326172\n",
            "Step=8299 loss=0.2802633899450302, accuracy=0.882578125\n",
            "Test loss = 0.2679092288017273, Test accuracy = 0.8779022097587585\n",
            "Step=8399 loss=0.270712351500988, accuracy=0.886796875\n",
            "Test loss = 0.24781033396720886, Test accuracy = 0.8880558609962463\n",
            "Step=8499 loss=0.2775257217884064, accuracy=0.8828125\n",
            "Test loss = 0.25908589363098145, Test accuracy = 0.8835207223892212\n",
            "Step=8599 loss=0.2806855148077011, accuracy=0.879375\n",
            "Test loss = 0.26888999342918396, Test accuracy = 0.8776889443397522\n",
            "Step=8699 loss=0.2697500604391098, accuracy=0.889453125\n",
            "Test loss = 0.2614341676235199, Test accuracy = 0.8819360136985779\n",
            "Step=8799 loss=0.26925193786621093, accuracy=0.886171875\n",
            "Test loss = 0.2586672008037567, Test accuracy = 0.8829617500305176\n",
            "Step=8899 loss=0.27157026559114456, accuracy=0.885859375\n",
            "Test loss = 0.2538037598133087, Test accuracy = 0.8850190043449402\n",
            "Step=8999 loss=0.2748983407020569, accuracy=0.8859375\n",
            "Test loss = 0.23963935673236847, Test accuracy = 0.8922280073165894\n",
            "Step=9099 loss=0.27895734295248986, accuracy=0.880859375\n",
            "Test loss = 0.24728788435459137, Test accuracy = 0.8887128233909607\n",
            "Step=9199 loss=0.27265708848834036, accuracy=0.882734375\n",
            "Test loss = 0.2613966464996338, Test accuracy = 0.88150954246521\n",
            "Step=9299 loss=0.27438717722892764, accuracy=0.88328125\n",
            "Test loss = 0.25485557317733765, Test accuracy = 0.8847769498825073\n",
            "Step=9399 loss=0.2758334122598171, accuracy=0.88484375\n",
            "Test loss = 0.2605842649936676, Test accuracy = 0.8817170262336731\n",
            "Step=9499 loss=0.2715705958008766, accuracy=0.884921875\n",
            "Test loss = 0.2481270283460617, Test accuracy = 0.8877792954444885\n",
            "Step=9599 loss=0.2758607617020607, accuracy=0.88234375\n",
            "Test loss = 0.24806399643421173, Test accuracy = 0.8884419798851013\n",
            "Step=9699 loss=0.2779682518541813, accuracy=0.882734375\n",
            "Test loss = 0.25463685393333435, Test accuracy = 0.8847481608390808\n",
            "Step=9799 loss=0.27824958860874177, accuracy=0.879296875\n",
            "Test loss = 0.2613052725791931, Test accuracy = 0.8816766738891602\n",
            "Step=9899 loss=0.26188158929347993, accuracy=0.890234375\n",
            "Test loss = 0.2516913115978241, Test accuracy = 0.8867765665054321\n",
            "Step=9999 loss=0.2613501980900764, accuracy=0.891171875\n",
            "Test loss = 0.25325340032577515, Test accuracy = 0.8863040208816528\n",
            "Step=10099 loss=0.2724384285509586, accuracy=0.88453125\n",
            "Test loss = 0.2578684687614441, Test accuracy = 0.8824949860572815\n",
            "Step=10199 loss=0.2533834186196327, accuracy=0.895234375\n",
            "Test loss = 0.24725842475891113, Test accuracy = 0.8890297412872314\n",
            "Step=10299 loss=0.26740736454725267, accuracy=0.889609375\n",
            "Test loss = 0.2688622772693634, Test accuracy = 0.8782364130020142\n",
            "Step=10399 loss=0.2664697350561619, accuracy=0.888671875\n",
            "Test loss = 0.2520691752433777, Test accuracy = 0.8849901556968689\n",
            "Step=10499 loss=0.266856477111578, accuracy=0.88828125\n",
            "Test loss = 0.25469186902046204, Test accuracy = 0.8852840662002563\n",
            "Step=10599 loss=0.26802361488342286, accuracy=0.8890625\n",
            "Test loss = 0.2397281527519226, Test accuracy = 0.8921012282371521\n",
            "Step=10699 loss=0.2979871901869774, accuracy=0.886875\n",
            "Test loss = 0.2710011601448059, Test accuracy = 0.8797865509986877\n",
            "Step=10799 loss=0.2745328293740749, accuracy=0.88625\n",
            "Test loss = 0.26718029379844666, Test accuracy = 0.8813597559928894\n",
            "Step=10899 loss=0.2695509901642799, accuracy=0.888125\n",
            "Test loss = 0.2467794567346573, Test accuracy = 0.8909314274787903\n",
            "Step=10999 loss=0.2715000434219837, accuracy=0.88703125\n",
            "Test loss = 0.2502858340740204, Test accuracy = 0.8888453245162964\n",
            "Step=11099 loss=0.26791901886463165, accuracy=0.888515625\n",
            "Test loss = 0.25720298290252686, Test accuracy = 0.885520339012146\n",
            "Step=11199 loss=0.2720436738431454, accuracy=0.885859375\n",
            "Test loss = 0.24399513006210327, Test accuracy = 0.8914327621459961\n",
            "Step=11299 loss=0.272722125351429, accuracy=0.886640625\n",
            "Test loss = 0.25675496459007263, Test accuracy = 0.885295569896698\n",
            "Step=11399 loss=0.259052969366312, accuracy=0.890625\n",
            "Test loss = 0.26912161707878113, Test accuracy = 0.8783516883850098\n",
            "Step=11499 loss=0.2593690802156925, accuracy=0.893046875\n",
            "Test loss = 0.2499152272939682, Test accuracy = 0.8879348635673523\n",
            "Step=11599 loss=0.2789788791537285, accuracy=0.884921875\n",
            "Test loss = 0.2430592030286789, Test accuracy = 0.8903033137321472\n",
            "Step=11699 loss=0.26310771524906157, accuracy=0.8896875\n",
            "Test loss = 0.24979497492313385, Test accuracy = 0.8876525163650513\n",
            "Step=11799 loss=0.2697035855054855, accuracy=0.886328125\n",
            "Test loss = 0.2662065327167511, Test accuracy = 0.8788126707077026\n",
            "Step=11899 loss=0.2693257564306259, accuracy=0.884453125\n",
            "Test loss = 0.26445263624191284, Test accuracy = 0.8795733451843262\n",
            "Step=11999 loss=0.26232415303587914, accuracy=0.89140625\n",
            "Test loss = 0.2594320476055145, Test accuracy = 0.8834054470062256\n",
            "Step=12099 loss=0.26395890951156614, accuracy=0.892734375\n",
            "Test loss = 0.24527260661125183, Test accuracy = 0.8892256617546082\n",
            "Step=12199 loss=0.269170448333025, accuracy=0.885625\n",
            "Test loss = 0.2540381848812103, Test accuracy = 0.8853071331977844\n",
            "Step=12299 loss=0.26795037418603895, accuracy=0.885625\n",
            "Test loss = 0.29162725806236267, Test accuracy = 0.8673969507217407\n",
            "Step=12399 loss=0.26467404812574385, accuracy=0.890546875\n",
            "Test loss = 0.24597561359405518, Test accuracy = 0.8890643119812012\n",
            "Step=12499 loss=0.267961658090353, accuracy=0.88890625\n",
            "Test loss = 0.25282523036003113, Test accuracy = 0.8856125473976135\n",
            "Step=12599 loss=0.2621817374229431, accuracy=0.88953125\n",
            "Test loss = 0.27452176809310913, Test accuracy = 0.8758103847503662\n",
            "Step=12699 loss=0.2657811433076859, accuracy=0.886796875\n",
            "Test loss = 0.25793296098709106, Test accuracy = 0.8832268118858337\n",
            "Step=12799 loss=0.2563826271891594, accuracy=0.893125\n",
            "Test loss = 0.26949596405029297, Test accuracy = 0.877487301826477\n",
            "Step=12899 loss=0.26625348299741747, accuracy=0.885625\n",
            "Test loss = 0.27543333172798157, Test accuracy = 0.8740988969802856\n",
            "Step=12999 loss=0.26837160557508466, accuracy=0.88703125\n",
            "Test loss = 0.2588008642196655, Test accuracy = 0.8824546337127686\n",
            "Step=13099 loss=0.26203058257699013, accuracy=0.891640625\n",
            "Test loss = 0.26260581612586975, Test accuracy = 0.8810312747955322\n",
            "Step=13199 loss=0.2663986286520958, accuracy=0.889921875\n",
            "Test loss = 0.2676946222782135, Test accuracy = 0.8788068890571594\n",
            "Step=13299 loss=0.2659244978427887, accuracy=0.89171875\n",
            "Test loss = 0.24214382469654083, Test accuracy = 0.8911388516426086\n",
            "Step=13399 loss=0.2569169746339321, accuracy=0.89140625\n",
            "Test loss = 0.25344032049179077, Test accuracy = 0.8853013515472412\n",
            "Step=13499 loss=0.2574555315077305, accuracy=0.8915625\n",
            "Test loss = 0.26215672492980957, Test accuracy = 0.8813136219978333\n",
            "Step=13599 loss=0.2610259060561657, accuracy=0.891875\n",
            "Test loss = 0.2649008631706238, Test accuracy = 0.8805299401283264\n",
            "Step=13699 loss=0.2595650236308575, accuracy=0.890703125\n",
            "Test loss = 0.25047507882118225, Test accuracy = 0.8868514895439148\n",
            "Step=13799 loss=0.27425794169306755, accuracy=0.88484375\n",
            "Test loss = 0.25819945335388184, Test accuracy = 0.8825699090957642\n",
            "Step=13899 loss=0.261989174336195, accuracy=0.88765625\n",
            "Test loss = 0.2693990468978882, Test accuracy = 0.8784323334693909\n",
            "Step=13999 loss=0.25119534626603124, accuracy=0.893515625\n",
            "Test loss = 0.26482006907463074, Test accuracy = 0.8808699250221252\n",
            "Step=14099 loss=0.2610296146571636, accuracy=0.890703125\n",
            "Test loss = 0.24503503739833832, Test accuracy = 0.8893985748291016\n",
            "Step=14199 loss=0.26947230637073516, accuracy=0.891328125\n",
            "Test loss = 0.25885701179504395, Test accuracy = 0.8848057985305786\n",
            "Step=14299 loss=0.27108473211526873, accuracy=0.88375\n",
            "Test loss = 0.26385319232940674, Test accuracy = 0.8822759985923767\n",
            "Step=14399 loss=0.2610101762413979, accuracy=0.89375\n",
            "Test loss = 0.24602533876895905, Test accuracy = 0.8901649713516235\n",
            "Step=14499 loss=0.2520834456384182, accuracy=0.8959375\n",
            "Test loss = 0.2574174404144287, Test accuracy = 0.8844830393791199\n",
            "Step=14599 loss=0.26427831649780276, accuracy=0.89015625\n",
            "Test loss = 0.2567887306213379, Test accuracy = 0.8853186368942261\n",
            "Step=14699 loss=0.25106774643063545, accuracy=0.894609375\n",
            "Test loss = 0.24825170636177063, Test accuracy = 0.8891392350196838\n",
            "Step=14799 loss=0.26145520448684695, accuracy=0.89109375\n",
            "Test loss = 0.2666652202606201, Test accuracy = 0.8787493109703064\n",
            "Step=14899 loss=0.25767456099390984, accuracy=0.890234375\n",
            "Test loss = 0.2621336877346039, Test accuracy = 0.8813366889953613\n",
            "Step=14999 loss=0.26220063358545304, accuracy=0.8921875\n",
            "Test loss = 0.25622180104255676, Test accuracy = 0.8838837742805481\n",
            "Step=15099 loss=0.26779925197362897, accuracy=0.889140625\n",
            "Test loss = 0.2568720281124115, Test accuracy = 0.8837973475456238\n",
            "Step=15199 loss=0.26346728682518006, accuracy=0.8903125\n",
            "Test loss = 0.2628724277019501, Test accuracy = 0.880195677280426\n",
            "Step=15299 loss=0.2442541378736496, accuracy=0.8990625\n",
            "Test loss = 0.2525199353694916, Test accuracy = 0.8857566118240356\n",
            "Step=15399 loss=0.26287478670477865, accuracy=0.89046875\n",
            "Test loss = 0.24595488607883453, Test accuracy = 0.8891161680221558\n",
            "Step=15499 loss=0.25055603325366976, accuracy=0.894609375\n",
            "Test loss = 0.2519693076610565, Test accuracy = 0.8871972560882568\n",
            "Step=15599 loss=0.2662572467327118, accuracy=0.89125\n",
            "Test loss = 0.23523396253585815, Test accuracy = 0.8949421644210815\n",
            "Step=15699 loss=0.25779965132474897, accuracy=0.8934375\n",
            "Test loss = 0.252973735332489, Test accuracy = 0.8858545422554016\n",
            "Step=15799 loss=0.2584735624492168, accuracy=0.896640625\n",
            "Test loss = 0.2505830228328705, Test accuracy = 0.8873989582061768\n",
            "Step=15899 loss=0.25951777905225754, accuracy=0.893359375\n",
            "Test loss = 0.2649058401584625, Test accuracy = 0.8807546496391296\n",
            "Step=15999 loss=0.2664944587647915, accuracy=0.888828125\n",
            "Test loss = 0.24364611506462097, Test accuracy = 0.8902226090431213\n",
            "Step=16099 loss=0.25203012317419055, accuracy=0.894765625\n",
            "Test loss = 0.25185713171958923, Test accuracy = 0.8864365816116333\n",
            "Step=16199 loss=0.2578097048401833, accuracy=0.89203125\n",
            "Test loss = 0.24578970670700073, Test accuracy = 0.8892083764076233\n",
            "Step=16299 loss=0.2640173152089119, accuracy=0.893125\n",
            "Test loss = 0.25442129373550415, Test accuracy = 0.8873182535171509\n",
            "Step=16399 loss=0.26238871827721594, accuracy=0.888359375\n",
            "Test loss = 0.24705538153648376, Test accuracy = 0.890781581401825\n",
            "Step=16499 loss=0.26401911571621894, accuracy=0.888984375\n",
            "Test loss = 0.26893651485443115, Test accuracy = 0.8801150321960449\n",
            "Step=16599 loss=0.247113149613142, accuracy=0.89875\n",
            "Test loss = 0.24851740896701813, Test accuracy = 0.8898941278457642\n",
            "Step=16699 loss=0.2562953832745552, accuracy=0.898046875\n",
            "Test loss = 0.25302019715309143, Test accuracy = 0.887179970741272\n",
            "Step=16799 loss=0.2489656116068363, accuracy=0.89515625\n",
            "Test loss = 0.2615050673484802, Test accuracy = 0.8822990655899048\n",
            "Step=16899 loss=0.2554605013132095, accuracy=0.896875\n",
            "Test loss = 0.26246219873428345, Test accuracy = 0.8817285299301147\n",
            "Step=16999 loss=0.25833555847406386, accuracy=0.89375\n",
            "Test loss = 0.25314560532569885, Test accuracy = 0.886067807674408\n",
            "Step=17099 loss=0.24723504930734636, accuracy=0.897890625\n",
            "Test loss = 0.24215298891067505, Test accuracy = 0.8912771344184875\n",
            "Step=17199 loss=0.26094132572412493, accuracy=0.89078125\n",
            "Test loss = 0.26701441407203674, Test accuracy = 0.8798499703407288\n",
            "Step=17299 loss=0.2532933185994625, accuracy=0.895\n",
            "Test loss = 0.25567567348480225, Test accuracy = 0.8849382996559143\n",
            "Step=17399 loss=0.2527769961953163, accuracy=0.896875\n",
            "Test loss = 0.2418675422668457, Test accuracy = 0.8912944793701172\n",
            "Step=17499 loss=0.2531729507446289, accuracy=0.89703125\n",
            "Test loss = 0.25113072991371155, Test accuracy = 0.8867477774620056\n",
            "Step=17599 loss=0.2579063706099987, accuracy=0.89296875\n",
            "Test loss = 0.26059138774871826, Test accuracy = 0.8824085593223572\n",
            "Step=17699 loss=0.24641283720731735, accuracy=0.89984375\n",
            "Test loss = 0.25660213828086853, Test accuracy = 0.8841200470924377\n",
            "Step=17799 loss=0.2608089035749435, accuracy=0.89125\n",
            "Test loss = 0.2535412311553955, Test accuracy = 0.8855952620506287\n",
            "Step=17899 loss=0.25788447007536885, accuracy=0.894921875\n",
            "Test loss = 0.25000840425491333, Test accuracy = 0.8872376084327698\n",
            "Step=17999 loss=0.2535959754884243, accuracy=0.895625\n",
            "Test loss = 0.24862197041511536, Test accuracy = 0.8873816728591919\n",
            "Step=18099 loss=0.24970194444060326, accuracy=0.89609375\n",
            "Test loss = 0.2721822261810303, Test accuracy = 0.8774296641349792\n",
            "Step=18199 loss=0.2539484678208828, accuracy=0.8953125\n",
            "Test loss = 0.25550004839897156, Test accuracy = 0.8855952620506287\n",
            "Step=18299 loss=0.2517834839224815, accuracy=0.894296875\n",
            "Test loss = 0.2382011115550995, Test accuracy = 0.8932133913040161\n",
            "Step=18399 loss=0.2466154558956623, accuracy=0.89875\n",
            "Test loss = 0.2528993487358093, Test accuracy = 0.8856356143951416\n",
            "Step=18499 loss=0.24800511091947555, accuracy=0.899921875\n",
            "Test loss = 0.24267449975013733, Test accuracy = 0.8911388516426086\n",
            "Step=18599 loss=0.25197155863046644, accuracy=0.894140625\n",
            "Test loss = 0.25404253602027893, Test accuracy = 0.8855836987495422\n",
            "Step=18699 loss=0.25210293412208556, accuracy=0.896875\n",
            "Test loss = 0.25662514567375183, Test accuracy = 0.8835265040397644\n",
            "Step=18799 loss=0.24974663957953452, accuracy=0.896328125\n",
            "Test loss = 0.24274739623069763, Test accuracy = 0.8909717202186584\n",
            "Step=18899 loss=0.2610466077923775, accuracy=0.8934375\n",
            "Test loss = 0.24269133806228638, Test accuracy = 0.8908276557922363\n",
            "Step=18999 loss=0.25540912479162214, accuracy=0.894140625\n",
            "Test loss = 0.24839124083518982, Test accuracy = 0.8874104619026184\n",
            "Step=19099 loss=0.24462001010775566, accuracy=0.897734375\n",
            "Test loss = 0.23945841193199158, Test accuracy = 0.8926371335983276\n",
            "Step=19199 loss=0.2473731791973114, accuracy=0.8965625\n",
            "Test loss = 0.2650397717952728, Test accuracy = 0.8802936673164368\n",
            "Step=19299 loss=0.24883622840046882, accuracy=0.900390625\n",
            "Test loss = 0.2516643702983856, Test accuracy = 0.8895714282989502\n",
            "Step=19399 loss=0.25069972038269045, accuracy=0.89625\n",
            "Test loss = 0.2784498631954193, Test accuracy = 0.8764961361885071\n",
            "Step=19499 loss=0.25324449971318247, accuracy=0.896796875\n",
            "Test loss = 0.2380908876657486, Test accuracy = 0.8956913352012634\n",
            "Step=19599 loss=0.2586691768467426, accuracy=0.8909375\n",
            "Test loss = 0.27523574233055115, Test accuracy = 0.8775103092193604\n",
            "Step=19699 loss=0.24794341385364532, accuracy=0.9015625\n",
            "Test loss = 0.2586069405078888, Test accuracy = 0.8847481608390808\n",
            "Step=19799 loss=0.2531919446587563, accuracy=0.897890625\n",
            "Test loss = 0.27227869629859924, Test accuracy = 0.8779655694961548\n",
            "Step=19899 loss=0.24725822791457175, accuracy=0.899140625\n",
            "Test loss = 0.2465202510356903, Test accuracy = 0.8907585144042969\n",
            "Step=19999 loss=0.2426338627934456, accuracy=0.900625\n",
            "Test loss = 0.26799276471138, Test accuracy = 0.8803800940513611\n",
            "Step=20099 loss=0.2512331491708755, accuracy=0.898515625\n",
            "Test loss = 0.25271034240722656, Test accuracy = 0.8874911665916443\n",
            "Step=20199 loss=0.24916948705911637, accuracy=0.897578125\n",
            "Test loss = 0.2404409646987915, Test accuracy = 0.8935015201568604\n",
            "Step=20299 loss=0.2525946344435215, accuracy=0.899296875\n",
            "Test loss = 0.27751103043556213, Test accuracy = 0.8755395412445068\n",
            "Step=20399 loss=0.2463230749964714, accuracy=0.899140625\n",
            "Test loss = 0.2501976788043976, Test accuracy = 0.8881365656852722\n",
            "Step=20499 loss=0.24316192045807838, accuracy=0.89859375\n",
            "Test loss = 0.2386951893568039, Test accuracy = 0.8936455845832825\n",
            "Step=20599 loss=0.24684663489460945, accuracy=0.900390625\n",
            "Test loss = 0.2759970426559448, Test accuracy = 0.8771011829376221\n",
            "Step=20699 loss=0.24805075630545617, accuracy=0.899375\n",
            "Test loss = 0.2531100809574127, Test accuracy = 0.8864769339561462\n",
            "Step=20799 loss=0.24936001434922217, accuracy=0.89796875\n",
            "Test loss = 0.2623405456542969, Test accuracy = 0.8819475173950195\n",
            "Step=20899 loss=0.24363544791936875, accuracy=0.9028125\n",
            "Test loss = 0.2428937703371048, Test accuracy = 0.8921818733215332\n",
            "Step=20999 loss=0.24769910231232642, accuracy=0.89953125\n",
            "Test loss = 0.25223881006240845, Test accuracy = 0.8874450325965881\n",
            "Step=21099 loss=0.25193296059966086, accuracy=0.89859375\n",
            "Test loss = 0.243573397397995, Test accuracy = 0.8910351395606995\n",
            "Step=21199 loss=0.24398553147912025, accuracy=0.902109375\n",
            "Test loss = 0.24744713306427002, Test accuracy = 0.888960599899292\n",
            "Step=21299 loss=0.24479734972119332, accuracy=0.8978125\n",
            "Test loss = 0.25111788511276245, Test accuracy = 0.8871223330497742\n",
            "Step=21399 loss=0.24510380417108535, accuracy=0.9015625\n",
            "Test loss = 0.2632513642311096, Test accuracy = 0.8820858001708984\n",
            "Step=21499 loss=0.25152098163962366, accuracy=0.89875\n",
            "Test loss = 0.2679022252559662, Test accuracy = 0.8799363970756531\n",
            "Step=21599 loss=0.24992000669240952, accuracy=0.897578125\n",
            "Test loss = 0.24366185069084167, Test accuracy = 0.8902283906936646\n",
            "Step=21699 loss=0.2486712183058262, accuracy=0.899765625\n",
            "Test loss = 0.24808573722839355, Test accuracy = 0.8892487287521362\n",
            "Step=21799 loss=0.2477075256407261, accuracy=0.89875\n",
            "Test loss = 0.2695671319961548, Test accuracy = 0.8792217969894409\n",
            "Step=21899 loss=0.2432183739542961, accuracy=0.903359375\n",
            "Test loss = 0.25651028752326965, Test accuracy = 0.8850535750389099\n",
            "Step=21999 loss=0.24648343086242674, accuracy=0.900234375\n",
            "Test loss = 0.24895831942558289, Test accuracy = 0.8886609673500061\n",
            "Step=22099 loss=0.24203712716698647, accuracy=0.90171875\n",
            "Test loss = 0.23862358927726746, Test accuracy = 0.8934784531593323\n",
            "Step=22199 loss=0.2472314015030861, accuracy=0.8984375\n",
            "Test loss = 0.24812348186969757, Test accuracy = 0.88841313123703\n",
            "Step=22299 loss=0.2566679111123085, accuracy=0.89609375\n",
            "Test loss = 0.2397739440202713, Test accuracy = 0.8930866122245789\n",
            "Step=22399 loss=0.23975888401269912, accuracy=0.903203125\n",
            "Test loss = 0.24217069149017334, Test accuracy = 0.8911792039871216\n",
            "Step=22499 loss=0.24247618690133094, accuracy=0.89859375\n",
            "Test loss = 0.25503525137901306, Test accuracy = 0.8856009840965271\n",
            "Step=22599 loss=0.2430577091872692, accuracy=0.90234375\n",
            "Test loss = 0.2517507076263428, Test accuracy = 0.8871511220932007\n",
            "Step=22699 loss=0.24034295707941056, accuracy=0.90375\n",
            "Test loss = 0.24752280116081238, Test accuracy = 0.8895137906074524\n",
            "Step=22799 loss=0.24758762642741203, accuracy=0.8978125\n",
            "Test loss = 0.2503216564655304, Test accuracy = 0.8880616426467896\n",
            "Step=22899 loss=0.24285613432526587, accuracy=0.90234375\n",
            "Test loss = 0.23721429705619812, Test accuracy = 0.8942564129829407\n",
            "Step=22999 loss=0.24322350665926934, accuracy=0.90140625\n",
            "Test loss = 0.25503164529800415, Test accuracy = 0.8857335448265076\n",
            "Step=23099 loss=0.25474701926112175, accuracy=0.8946875\n",
            "Test loss = 0.25639134645462036, Test accuracy = 0.8848000168800354\n",
            "Step=23199 loss=0.24024857208132744, accuracy=0.90421875\n",
            "Test loss = 0.24588213860988617, Test accuracy = 0.8903378844261169\n",
            "Step=23299 loss=0.24749040201306344, accuracy=0.89796875\n",
            "Test loss = 0.24488887190818787, Test accuracy = 0.8900209069252014\n",
            "Step=23399 loss=0.2433922879397869, accuracy=0.89953125\n",
            "Test loss = 0.2474423050880432, Test accuracy = 0.8888511061668396\n",
            "Step=23499 loss=0.24330458626151086, accuracy=0.9009375\n",
            "Test loss = 0.2700800597667694, Test accuracy = 0.8793255686759949\n",
            "Step=23599 loss=0.24147731125354766, accuracy=0.901484375\n",
            "Test loss = 0.23696550726890564, Test accuracy = 0.8943082690238953\n",
            "Step=23699 loss=0.24391964554786683, accuracy=0.9\n",
            "Test loss = 0.24671903252601624, Test accuracy = 0.8893409371376038\n",
            "Step=23799 loss=0.24726821899414062, accuracy=0.89953125\n",
            "Test loss = 0.2482355237007141, Test accuracy = 0.8885629773139954\n",
            "Step=23899 loss=0.2429159852862358, accuracy=0.899453125\n",
            "Test loss = 0.24632950127124786, Test accuracy = 0.8902686834335327\n",
            "Step=23999 loss=0.24487267911434174, accuracy=0.90015625\n",
            "Test loss = 0.23766563832759857, Test accuracy = 0.8936859369277954\n",
            "Step=24099 loss=0.23584624841809274, accuracy=0.9028125\n",
            "Test loss = 0.24996884167194366, Test accuracy = 0.8882633447647095\n",
            "Step=24199 loss=0.24063870549201966, accuracy=0.901796875\n",
            "Test loss = 0.26747429370880127, Test accuracy = 0.8801438212394714\n",
            "Step=24299 loss=0.24241686701774598, accuracy=0.90203125\n",
            "Test loss = 0.24326863884925842, Test accuracy = 0.8908392190933228\n",
            "Step=24399 loss=0.2413131995499134, accuracy=0.90375\n",
            "Test loss = 0.25739914178848267, Test accuracy = 0.8848403692245483\n",
            "Step=24499 loss=0.24447949096560478, accuracy=0.900078125\n",
            "Test loss = 0.24093885719776154, Test accuracy = 0.8924930691719055\n",
            "Step=24599 loss=0.2537422025948763, accuracy=0.900390625\n",
            "Test loss = 0.24759933352470398, Test accuracy = 0.8889663815498352\n",
            "Step=24699 loss=0.23470545023679734, accuracy=0.903984375\n",
            "Test loss = 0.2427871972322464, Test accuracy = 0.8919917345046997\n",
            "Step=24799 loss=0.24594702899456025, accuracy=0.901640625\n",
            "Test loss = 0.24899768829345703, Test accuracy = 0.888292133808136\n",
            "Step=24899 loss=0.23421162933111192, accuracy=0.904453125\n",
            "Test loss = 0.24920813739299774, Test accuracy = 0.8894388675689697\n",
            "Step=24999 loss=0.2433307495713234, accuracy=0.89953125\n",
            "Test loss = 0.25514164566993713, Test accuracy = 0.8862291574478149\n",
            "Step=25099 loss=0.2412372013926506, accuracy=0.903359375\n",
            "Test loss = 0.24766097962856293, Test accuracy = 0.8902398943901062\n",
            "Step=25199 loss=0.2390842144191265, accuracy=0.902265625\n",
            "Test loss = 0.23877397179603577, Test accuracy = 0.8937205076217651\n",
            "Step=25299 loss=0.2421233692765236, accuracy=0.9040625\n",
            "Test loss = 0.255602091550827, Test accuracy = 0.8852609992027283\n",
            "Step=25399 loss=0.23549121648073196, accuracy=0.904921875\n",
            "Test loss = 0.27024033665657043, Test accuracy = 0.8789682388305664\n",
            "Step=25499 loss=0.24843355640769005, accuracy=0.89546875\n",
            "Test loss = 0.26097115874290466, Test accuracy = 0.8834400177001953\n",
            "Step=25599 loss=0.23750319480895996, accuracy=0.905546875\n",
            "Test loss = 0.2618071138858795, Test accuracy = 0.8820973634719849\n",
            "Step=25699 loss=0.23930131509900093, accuracy=0.903359375\n",
            "Test loss = 0.2641235291957855, Test accuracy = 0.881371259689331\n",
            "Step=25799 loss=0.23592489570379258, accuracy=0.903515625\n",
            "Test loss = 0.26136788725852966, Test accuracy = 0.8835725784301758\n",
            "Step=25899 loss=0.22805978372693061, accuracy=0.909296875\n",
            "Test loss = 0.24926386773586273, Test accuracy = 0.889024019241333\n",
            "Step=25999 loss=0.24515149429440497, accuracy=0.901015625\n",
            "Test loss = 0.2641238570213318, Test accuracy = 0.8819475173950195\n",
            "Step=26099 loss=0.24421215564012527, accuracy=0.9015625\n",
            "Test loss = 0.2559860050678253, Test accuracy = 0.8854972720146179\n",
            "Step=26199 loss=0.2329942390322685, accuracy=0.906640625\n",
            "Test loss = 0.25956469774246216, Test accuracy = 0.884408175945282\n",
            "Step=26299 loss=0.23685860842466355, accuracy=0.902734375\n",
            "Test loss = 0.2550911009311676, Test accuracy = 0.8862003087997437\n",
            "Step=26399 loss=0.23918168127536774, accuracy=0.90125\n",
            "Test loss = 0.23866842687129974, Test accuracy = 0.8940317034721375\n",
            "Step=26499 loss=0.23241563960909845, accuracy=0.90671875\n",
            "Test loss = 0.26100730895996094, Test accuracy = 0.8837454319000244\n",
            "Step=26599 loss=0.243923409730196, accuracy=0.901875\n",
            "Test loss = 0.25040414929389954, Test accuracy = 0.8880558609962463\n",
            "Step=26699 loss=0.23622042804956436, accuracy=0.907734375\n",
            "Test loss = 0.2665426433086395, Test accuracy = 0.880581796169281\n",
            "Step=26799 loss=0.23239442363381385, accuracy=0.90890625\n",
            "Test loss = 0.23952984809875488, Test accuracy = 0.8937492966651917\n",
            "Step=26899 loss=0.23533767625689506, accuracy=0.904375\n",
            "Test loss = 0.23478670418262482, Test accuracy = 0.8959563970565796\n",
            "Step=26999 loss=0.23351512119174003, accuracy=0.904765625\n",
            "Test loss = 0.27401527762413025, Test accuracy = 0.8779309988021851\n",
            "Step=27099 loss=0.2400486759841442, accuracy=0.900625\n",
            "Test loss = 0.26471784710884094, Test accuracy = 0.8812675476074219\n",
            "Step=27199 loss=0.24020494118332864, accuracy=0.90171875\n",
            "Test loss = 0.24541456997394562, Test accuracy = 0.8904761672019958\n",
            "Step=27299 loss=0.23868115916848182, accuracy=0.90453125\n",
            "Test loss = 0.26077789068222046, Test accuracy = 0.8829675316810608\n",
            "Step=27399 loss=0.23044965878129006, accuracy=0.90734375\n",
            "Test loss = 0.24201858043670654, Test accuracy = 0.892585277557373\n",
            "Step=27499 loss=0.2425016340613365, accuracy=0.901953125\n",
            "Test loss = 0.2568816542625427, Test accuracy = 0.8848864436149597\n",
            "Step=27599 loss=0.24246286898851394, accuracy=0.900859375\n",
            "Test loss = 0.2569996118545532, Test accuracy = 0.8854396343231201\n",
            "Step=27699 loss=0.23742291048169137, accuracy=0.9046875\n",
            "Test loss = 0.26152658462524414, Test accuracy = 0.8831634521484375\n",
            "Step=27799 loss=0.23437280319631099, accuracy=0.9065625\n",
            "Test loss = 0.27746716141700745, Test accuracy = 0.8764269351959229\n",
            "Step=27899 loss=0.2572504571080208, accuracy=0.901328125\n",
            "Test loss = 0.2706950604915619, Test accuracy = 0.8811234831809998\n",
            "Step=27999 loss=0.23341475240886211, accuracy=0.90828125\n",
            "Test loss = 0.2643336057662964, Test accuracy = 0.8844484686851501\n",
            "Step=28099 loss=0.24014855340123176, accuracy=0.9025\n",
            "Test loss = 0.2835790812969208, Test accuracy = 0.8750554919242859\n",
            "Step=28199 loss=0.23206877544522286, accuracy=0.905546875\n",
            "Test loss = 0.2543850243091583, Test accuracy = 0.8880558609962463\n",
            "Step=28299 loss=0.23759034901857376, accuracy=0.903515625\n",
            "Test loss = 0.2521163821220398, Test accuracy = 0.8893870115280151\n",
            "Step=28399 loss=0.23381667211651802, accuracy=0.906171875\n",
            "Test loss = 0.266005277633667, Test accuracy = 0.8825526237487793\n",
            "Step=28499 loss=0.24032675936818124, accuracy=0.90796875\n",
            "Test loss = 0.27579477429389954, Test accuracy = 0.8767669796943665\n",
            "Step=28599 loss=0.24246302410960197, accuracy=0.901953125\n",
            "Test loss = 0.23791831731796265, Test accuracy = 0.8951265811920166\n",
            "Step=28699 loss=0.23384685918688775, accuracy=0.905703125\n",
            "Test loss = 0.26544442772865295, Test accuracy = 0.8818149566650391\n",
            "Step=28799 loss=0.2379193624854088, accuracy=0.905\n",
            "Test loss = 0.2866775691509247, Test accuracy = 0.8722952008247375\n",
            "Step=28899 loss=0.237903101593256, accuracy=0.9046875\n",
            "Test loss = 0.25508514046669006, Test accuracy = 0.8867074251174927\n",
            "Step=28999 loss=0.23305840581655501, accuracy=0.90796875\n",
            "Test loss = 0.24687495827674866, Test accuracy = 0.8902513980865479\n",
            "Step=29099 loss=0.23173513069748877, accuracy=0.90890625\n",
            "Test loss = 0.25322309136390686, Test accuracy = 0.8880385756492615\n",
            "Step=29199 loss=0.23311001747846605, accuracy=0.90703125\n",
            "Test loss = 0.2608758211135864, Test accuracy = 0.8843101859092712\n",
            "Step=29299 loss=0.23281472206115722, accuracy=0.908125\n",
            "Test loss = 0.2522627115249634, Test accuracy = 0.8882690668106079\n",
            "Step=29399 loss=0.23257680207490922, accuracy=0.9075\n",
            "Test loss = 0.24305059015750885, Test accuracy = 0.8928849101066589\n",
            "Step=29499 loss=0.2367851783335209, accuracy=0.904453125\n",
            "Test loss = 0.27125823497772217, Test accuracy = 0.8789682388305664\n",
            "Step=29599 loss=0.23129163414239884, accuracy=0.90640625\n",
            "Test loss = 0.2514488995075226, Test accuracy = 0.889404296875\n",
            "Step=29699 loss=0.2349486780166626, accuracy=0.906953125\n",
            "Test loss = 0.2417730838060379, Test accuracy = 0.8936628699302673\n",
            "Step=29799 loss=0.23533506855368613, accuracy=0.902890625\n",
            "Test loss = 0.25465863943099976, Test accuracy = 0.8868399858474731\n",
            "Step=29899 loss=0.2314298640191555, accuracy=0.908359375\n",
            "Test loss = 0.24928152561187744, Test accuracy = 0.8900439739227295\n",
            "Step=29999 loss=0.2347717148065567, accuracy=0.90578125\n",
            "Test loss = 0.25330767035484314, Test accuracy = 0.8876121640205383\n",
            "Step=30099 loss=0.2338753116130829, accuracy=0.90484375\n",
            "Test loss = 0.25857776403427124, Test accuracy = 0.8845176696777344\n",
            "Step=30199 loss=0.23712295144796372, accuracy=0.903515625\n",
            "Test loss = 0.2650584578514099, Test accuracy = 0.8821146488189697\n",
            "Step=30299 loss=0.24085618659853936, accuracy=0.90265625\n",
            "Test loss = 0.25051936507225037, Test accuracy = 0.8887646794319153\n",
            "Step=30399 loss=0.23497603416442872, accuracy=0.906875\n",
            "Test loss = 0.24023136496543884, Test accuracy = 0.8942736983299255\n",
            "Step=30499 loss=0.22560990422964097, accuracy=0.908359375\n",
            "Test loss = 0.25784456729888916, Test accuracy = 0.8859352469444275\n",
            "Step=30599 loss=0.22445615261793136, accuracy=0.9103125\n",
            "Test loss = 0.2753552794456482, Test accuracy = 0.8782882690429688\n",
            "Step=30699 loss=0.23824742585420608, accuracy=0.90125\n",
            "Test loss = 0.26261916756629944, Test accuracy = 0.8839125633239746\n",
            "Step=30799 loss=0.23892606124281884, accuracy=0.901328125\n",
            "Test loss = 0.25185665488243103, Test accuracy = 0.8891104459762573\n",
            "Step=30899 loss=0.2340435591340065, accuracy=0.904765625\n",
            "Test loss = 0.24743659794330597, Test accuracy = 0.8906835913658142\n",
            "Step=30999 loss=0.23192249223589897, accuracy=0.90578125\n",
            "Test loss = 0.24880851805210114, Test accuracy = 0.8904012441635132\n",
            "Step=31099 loss=0.2410035164654255, accuracy=0.902578125\n",
            "Test loss = 0.2564810812473297, Test accuracy = 0.8870013356208801\n",
            "Step=31199 loss=0.2386580476164818, accuracy=0.9075\n",
            "Test loss = 0.2621929347515106, Test accuracy = 0.8828061819076538\n",
            "Step=31299 loss=0.23241886705160142, accuracy=0.90734375\n",
            "Test loss = 0.25389811396598816, Test accuracy = 0.8876525163650513\n",
            "Step=31399 loss=0.22852636128664017, accuracy=0.91015625\n",
            "Test loss = 0.26159361004829407, Test accuracy = 0.8843505382537842\n",
            "Step=31499 loss=0.22688506484031679, accuracy=0.909921875\n",
            "Test loss = 0.25460776686668396, Test accuracy = 0.8872606158256531\n",
            "Step=31599 loss=0.24035473823547363, accuracy=0.901875\n",
            "Test loss = 0.24970632791519165, Test accuracy = 0.8895944952964783\n",
            "Step=31699 loss=0.23167014598846436, accuracy=0.906484375\n",
            "Test loss = 0.2432740479707718, Test accuracy = 0.8929252624511719\n",
            "Step=31799 loss=0.22666129752993583, accuracy=0.90859375\n",
            "Test loss = 0.25924763083457947, Test accuracy = 0.8855894804000854\n",
            "Step=31899 loss=0.23436910957098006, accuracy=0.90359375\n",
            "Test loss = 0.2801242172718048, Test accuracy = 0.8760063052177429\n",
            "Step=31999 loss=0.23150519967079164, accuracy=0.90578125\n",
            "Test loss = 0.26622265577316284, Test accuracy = 0.8823047876358032\n",
            "Step=32099 loss=0.22335303381085395, accuracy=0.910390625\n",
            "Test loss = 0.2578473687171936, Test accuracy = 0.8860158920288086\n",
            "Step=32199 loss=0.22611074179410934, accuracy=0.910859375\n",
            "Test loss = 0.2487683743238449, Test accuracy = 0.8901534676551819\n",
            "Step=32299 loss=0.22707301124930382, accuracy=0.909375\n",
            "Test loss = 0.25777173042297363, Test accuracy = 0.8862752318382263\n",
            "Step=32399 loss=0.2289535492658615, accuracy=0.908203125\n",
            "Test loss = 0.2674561142921448, Test accuracy = 0.8811695575714111\n",
            "Step=32499 loss=0.23247698687016963, accuracy=0.909453125\n",
            "Test loss = 0.25396424531936646, Test accuracy = 0.8876640200614929\n",
            "Step=32599 loss=0.23299288615584374, accuracy=0.9075\n",
            "Test loss = 0.246299609541893, Test accuracy = 0.8914269804954529\n",
            "Step=32699 loss=0.22994354590773583, accuracy=0.90765625\n",
            "Test loss = 0.24131901562213898, Test accuracy = 0.8931673169136047\n",
            "Step=32799 loss=0.2306920412182808, accuracy=0.908828125\n",
            "Test loss = 0.2680586278438568, Test accuracy = 0.8813194036483765\n",
            "Step=32899 loss=0.2318193807452917, accuracy=0.90515625\n",
            "Test loss = 0.24511408805847168, Test accuracy = 0.8925045728683472\n",
            "Step=32999 loss=0.227630185931921, accuracy=0.90875\n",
            "Test loss = 0.23827503621578217, Test accuracy = 0.8955011367797852\n",
            "Step=33099 loss=0.22341732032597064, accuracy=0.909765625\n",
            "Test loss = 0.25457045435905457, Test accuracy = 0.8876121640205383\n",
            "Step=33199 loss=0.23349093101918697, accuracy=0.90734375\n",
            "Test loss = 0.25782155990600586, Test accuracy = 0.8860158920288086\n",
            "Step=33299 loss=0.22453296288847924, accuracy=0.91171875\n",
            "Test loss = 0.24782714247703552, Test accuracy = 0.8907585144042969\n",
            "Step=33399 loss=0.2197137901186943, accuracy=0.914375\n",
            "Test loss = 0.2643534243106842, Test accuracy = 0.8835898637771606\n",
            "Step=33499 loss=0.23075813174247742, accuracy=0.907890625\n",
            "Test loss = 0.2440306693315506, Test accuracy = 0.8923777937889099\n",
            "Step=33599 loss=0.22040332823991776, accuracy=0.9146875\n",
            "Test loss = 0.25230807065963745, Test accuracy = 0.8888914585113525\n",
            "Step=33699 loss=0.224370673969388, accuracy=0.91265625\n",
            "Test loss = 0.25044089555740356, Test accuracy = 0.8898422718048096\n",
            "Step=33799 loss=0.224414142370224, accuracy=0.912109375\n",
            "Test loss = 0.2600325345993042, Test accuracy = 0.8847078084945679\n",
            "Step=33899 loss=0.229995726197958, accuracy=0.909375\n",
            "Test loss = 0.25941815972328186, Test accuracy = 0.8852207064628601\n",
            "Step=33999 loss=0.2232364533096552, accuracy=0.91265625\n",
            "Test loss = 0.25198787450790405, Test accuracy = 0.8893005847930908\n",
            "Step=34099 loss=0.22974630534648896, accuracy=0.907421875\n",
            "Test loss = 0.24610795080661774, Test accuracy = 0.8917785286903381\n",
            "Step=34199 loss=0.23199014499783516, accuracy=0.91046875\n",
            "Test loss = 0.2709849178791046, Test accuracy = 0.8787435293197632\n",
            "Step=34299 loss=0.22792838677763938, accuracy=0.90703125\n",
            "Test loss = 0.2794191539287567, Test accuracy = 0.8761676549911499\n",
            "Step=34399 loss=0.22526696354150771, accuracy=0.911484375\n",
            "Test loss = 0.25017595291137695, Test accuracy = 0.8895714282989502\n",
            "Step=34499 loss=0.22291736885905267, accuracy=0.912578125\n",
            "Test loss = 0.2626416087150574, Test accuracy = 0.8839932680130005\n",
            "Step=34599 loss=0.22014642715454102, accuracy=0.9121875\n",
            "Test loss = 0.26095402240753174, Test accuracy = 0.885036289691925\n",
            "Step=34699 loss=0.2311023586988449, accuracy=0.90703125\n",
            "Test loss = 0.2504896819591522, Test accuracy = 0.8901649713516235\n",
            "Step=34799 loss=0.22053957849740982, accuracy=0.913125\n",
            "Test loss = 0.2414751946926117, Test accuracy = 0.8949940204620361\n",
            "Step=34899 loss=0.22431069523096084, accuracy=0.90921875\n",
            "Test loss = 0.26471662521362305, Test accuracy = 0.8836532235145569\n",
            "Step=34999 loss=0.2232058271765709, accuracy=0.90875\n",
            "Test loss = 0.25396618247032166, Test accuracy = 0.8881538510322571\n",
            "Step=35099 loss=0.23426269203424455, accuracy=0.9065625\n",
            "Test loss = 0.273107647895813, Test accuracy = 0.8837742805480957\n",
            "Step=35199 loss=0.22505295254290103, accuracy=0.910703125\n",
            "Test loss = 0.2481953203678131, Test accuracy = 0.8936917185783386\n",
            "Step=35299 loss=0.23355451136827468, accuracy=0.907734375\n",
            "Test loss = 0.2539684474468231, Test accuracy = 0.8915537595748901\n",
            "Step=35399 loss=0.224864149838686, accuracy=0.91078125\n",
            "Test loss = 0.2627391517162323, Test accuracy = 0.8868802785873413\n",
            "Step=35499 loss=0.22470930948853493, accuracy=0.9115625\n",
            "Test loss = 0.2507287561893463, Test accuracy = 0.8928906917572021\n",
            "Step=35599 loss=0.230848458558321, accuracy=0.904140625\n",
            "Test loss = 0.2642022371292114, Test accuracy = 0.8858084678649902\n",
            "Step=35699 loss=0.236523334980011, accuracy=0.9078125\n",
            "Test loss = 0.25258582830429077, Test accuracy = 0.8896694183349609\n",
            "Step=35799 loss=0.22735951840877533, accuracy=0.91125\n",
            "Test loss = 0.2554472088813782, Test accuracy = 0.8892256617546082\n",
            "Step=35899 loss=0.2220088189840317, accuracy=0.911875\n",
            "Test loss = 0.25946104526519775, Test accuracy = 0.887139618396759\n",
            "Step=35999 loss=0.21886121198534966, accuracy=0.914765625\n",
            "Test loss = 0.24886354804039001, Test accuracy = 0.891893744468689\n",
            "Step=36099 loss=0.22712474524974824, accuracy=0.909296875\n",
            "Test loss = 0.24798443913459778, Test accuracy = 0.8924757838249207\n",
            "Step=36199 loss=0.2305588500201702, accuracy=0.90734375\n",
            "Test loss = 0.24545349180698395, Test accuracy = 0.8932883143424988\n",
            "Step=36299 loss=0.22833284482359886, accuracy=0.90921875\n",
            "Test loss = 0.2719626724720001, Test accuracy = 0.880864143371582\n",
            "Step=36399 loss=0.2257737983763218, accuracy=0.91078125\n",
            "Test loss = 0.25703489780426025, Test accuracy = 0.886793851852417\n",
            "Step=36499 loss=0.2230146822333336, accuracy=0.91140625\n",
            "Test loss = 0.2537992596626282, Test accuracy = 0.8887473940849304\n",
            "Step=36599 loss=0.22198061585426332, accuracy=0.91296875\n",
            "Test loss = 0.25387272238731384, Test accuracy = 0.8889490962028503\n",
            "Step=36699 loss=0.22792053893208503, accuracy=0.90984375\n",
            "Test loss = 0.24398475885391235, Test accuracy = 0.893455445766449\n",
            "Step=36799 loss=0.21965481646358967, accuracy=0.913359375\n",
            "Test loss = 0.2652105689048767, Test accuracy = 0.8825122714042664\n",
            "Step=36899 loss=0.22297727718949317, accuracy=0.91375\n",
            "Test loss = 0.25194722414016724, Test accuracy = 0.8896578550338745\n",
            "Step=36999 loss=0.22657806813716888, accuracy=0.912265625\n",
            "Test loss = 0.2452537566423416, Test accuracy = 0.8927178382873535\n",
            "Step=37099 loss=0.2152674312889576, accuracy=0.913828125\n",
            "Test loss = 0.27489855885505676, Test accuracy = 0.8788241744041443\n",
            "Step=37199 loss=0.22124232292175294, accuracy=0.9121875\n",
            "Test loss = 0.24487833678722382, Test accuracy = 0.8933862447738647\n",
            "Step=37299 loss=0.2318711534142494, accuracy=0.907265625\n",
            "Test loss = 0.25814345479011536, Test accuracy = 0.8866555690765381\n",
            "Step=37399 loss=0.22775330439209937, accuracy=0.90671875\n",
            "Test loss = 0.2574057877063751, Test accuracy = 0.8870128393173218\n",
            "Step=37499 loss=0.2203716231882572, accuracy=0.913359375\n",
            "Test loss = 0.2533421814441681, Test accuracy = 0.8888453245162964\n",
            "Step=37599 loss=0.2218456406891346, accuracy=0.911484375\n",
            "Test loss = 0.24436692893505096, Test accuracy = 0.8933228850364685\n",
            "Step=37699 loss=0.22668177612125873, accuracy=0.912578125\n",
            "Test loss = 0.2466714084148407, Test accuracy = 0.8918073177337646\n",
            "Step=37799 loss=0.218258176445961, accuracy=0.914921875\n",
            "Test loss = 0.2478497177362442, Test accuracy = 0.8918764591217041\n",
            "Step=37899 loss=0.22525632485747338, accuracy=0.910234375\n",
            "Test loss = 0.25520145893096924, Test accuracy = 0.8882172107696533\n",
            "Step=37999 loss=0.22415035471320152, accuracy=0.910625\n",
            "Test loss = 0.24842068552970886, Test accuracy = 0.8913693428039551\n",
            "Step=38099 loss=0.2195627745985985, accuracy=0.9109375\n",
            "Test loss = 0.24133431911468506, Test accuracy = 0.8952418565750122\n",
            "Step=38199 loss=0.22034965917468072, accuracy=0.913046875\n",
            "Test loss = 0.24521803855895996, Test accuracy = 0.8934900164604187\n",
            "Step=38299 loss=0.21533257998526095, accuracy=0.912421875\n",
            "Test loss = 0.24261215329170227, Test accuracy = 0.8951899409294128\n",
            "Step=38399 loss=0.22516313523054124, accuracy=0.9115625\n",
            "Test loss = 0.2533220648765564, Test accuracy = 0.8890066742897034\n",
            "Step=38499 loss=0.2194945963472128, accuracy=0.91234375\n",
            "Test loss = 0.25066351890563965, Test accuracy = 0.8902629613876343\n",
            "Step=38599 loss=0.22687513142824173, accuracy=0.910625\n",
            "Test loss = 0.2606595456600189, Test accuracy = 0.8854972720146179\n",
            "Step=38699 loss=0.21868291810154916, accuracy=0.914140625\n",
            "Test loss = 0.24151422083377838, Test accuracy = 0.8951669335365295\n",
            "Step=38799 loss=0.2262131640315056, accuracy=0.911640625\n",
            "Test loss = 0.2627471387386322, Test accuracy = 0.8854223489761353\n",
            "Step=38899 loss=0.21808525636792184, accuracy=0.914296875\n",
            "Test loss = 0.2486172467470169, Test accuracy = 0.8916747570037842\n",
            "Step=38999 loss=0.2200886631011963, accuracy=0.914375\n",
            "Test loss = 0.26098331809043884, Test accuracy = 0.8877965807914734\n",
            "Step=39099 loss=0.2166459545493126, accuracy=0.915\n",
            "Test loss = 0.2794499695301056, Test accuracy = 0.8816536068916321\n",
            "Step=39199 loss=0.218794714063406, accuracy=0.9140625\n",
            "Test loss = 0.26164668798446655, Test accuracy = 0.8895426392555237\n",
            "Step=39299 loss=0.2228582438826561, accuracy=0.915234375\n",
            "Test loss = 0.2545522153377533, Test accuracy = 0.8923490047454834\n",
            "Step=39399 loss=0.22147711768746375, accuracy=0.915234375\n",
            "Test loss = 0.24717889726161957, Test accuracy = 0.8960197567939758\n",
            "Step=39499 loss=0.22639861732721328, accuracy=0.9121875\n",
            "Test loss = 0.2624056339263916, Test accuracy = 0.8881538510322571\n",
            "Step=39599 loss=0.21665136247873307, accuracy=0.914453125\n",
            "Test loss = 0.2602788507938385, Test accuracy = 0.8886033296585083\n",
            "Step=39699 loss=0.22294466227293014, accuracy=0.91140625\n",
            "Test loss = 0.2651338279247284, Test accuracy = 0.8867132067680359\n",
            "Step=39799 loss=0.22132093593478203, accuracy=0.913828125\n",
            "Test loss = 0.2578223645687103, Test accuracy = 0.8896232843399048\n",
            "Step=39899 loss=0.2259208430349827, accuracy=0.91171875\n",
            "Test loss = 0.27969124913215637, Test accuracy = 0.8798729777336121\n",
            "Step=39999 loss=0.22177085012197495, accuracy=0.913203125\n",
            "Test loss = 0.25309988856315613, Test accuracy = 0.8914327621459961\n",
            "Step=40099 loss=0.22540154322981834, accuracy=0.912109375\n",
            "Test loss = 0.25396186113357544, Test accuracy = 0.8913174867630005\n",
            "Step=40199 loss=0.2112250255048275, accuracy=0.91921875\n",
            "Test loss = 0.26996687054634094, Test accuracy = 0.8841488361358643\n",
            "Step=40299 loss=0.22585845425724982, accuracy=0.906875\n",
            "Test loss = 0.2496812492609024, Test accuracy = 0.8925276398658752\n",
            "Step=40399 loss=0.2154335416853428, accuracy=0.91671875\n",
            "Test loss = 0.2536374032497406, Test accuracy = 0.8908565044403076\n",
            "Step=40499 loss=0.2191500882804394, accuracy=0.912421875\n",
            "Test loss = 0.25304868817329407, Test accuracy = 0.8909371495246887\n",
            "Step=40599 loss=0.2204343304783106, accuracy=0.912734375\n",
            "Test loss = 0.2521626055240631, Test accuracy = 0.8909544348716736\n",
            "Step=40699 loss=0.22459998086094857, accuracy=0.91109375\n",
            "Test loss = 0.2587590515613556, Test accuracy = 0.8880327939987183\n",
            "Step=40799 loss=0.2138766324520111, accuracy=0.9184375\n",
            "Test loss = 0.25685784220695496, Test accuracy = 0.8894100785255432\n",
            "Step=40899 loss=0.21878034755587578, accuracy=0.91328125\n",
            "Test loss = 0.2508648931980133, Test accuracy = 0.8922107219696045\n",
            "Step=40999 loss=0.22510805621743202, accuracy=0.91203125\n",
            "Test loss = 0.2567928433418274, Test accuracy = 0.8893351554870605\n",
            "Step=41099 loss=0.21645185396075248, accuracy=0.914296875\n",
            "Test loss = 0.2535882890224457, Test accuracy = 0.8905280232429504\n",
            "Step=41199 loss=0.21441805347800255, accuracy=0.917734375\n",
            "Test loss = 0.24826879799365997, Test accuracy = 0.8928157687187195\n",
            "Step=41299 loss=0.2188385969400406, accuracy=0.915\n",
            "Test loss = 0.25660037994384766, Test accuracy = 0.8882057070732117\n",
            "Step=41399 loss=0.21849515706300734, accuracy=0.91609375\n",
            "Test loss = 0.2536041736602783, Test accuracy = 0.8899863362312317\n",
            "Step=41499 loss=0.2268007028847933, accuracy=0.908203125\n",
            "Test loss = 0.25087592005729675, Test accuracy = 0.8911792039871216\n",
            "Step=41599 loss=0.22332997888326644, accuracy=0.9125\n",
            "Test loss = 0.2541881799697876, Test accuracy = 0.8896867036819458\n",
            "Step=41699 loss=0.22172838509082793, accuracy=0.91640625\n",
            "Test loss = 0.26459890604019165, Test accuracy = 0.884408175945282\n",
            "Step=41799 loss=0.21360870942473412, accuracy=0.918203125\n",
            "Test loss = 0.24859744310379028, Test accuracy = 0.8922856450080872\n",
            "Step=41899 loss=0.21631616562604905, accuracy=0.914375\n",
            "Test loss = 0.257874459028244, Test accuracy = 0.8882172107696533\n",
            "Step=41999 loss=0.21409289345145224, accuracy=0.916953125\n",
            "Test loss = 0.2706923484802246, Test accuracy = 0.8812963366508484\n",
            "Step=42099 loss=0.21271655425429345, accuracy=0.91625\n",
            "Test loss = 0.2532294690608978, Test accuracy = 0.8905971646308899\n",
            "Step=42199 loss=0.2127237704396248, accuracy=0.918203125\n",
            "Test loss = 0.2569057047367096, Test accuracy = 0.8884650468826294\n",
            "Step=42299 loss=0.21164251416921614, accuracy=0.9178125\n",
            "Test loss = 0.26410818099975586, Test accuracy = 0.885174572467804\n",
            "Step=42399 loss=0.2191689546406269, accuracy=0.913828125\n",
            "Test loss = 0.2574785053730011, Test accuracy = 0.8880212903022766\n",
            "Step=42499 loss=0.21989869430661202, accuracy=0.914921875\n",
            "Test loss = 0.26395922899246216, Test accuracy = 0.8855549097061157\n",
            "Step=42599 loss=0.21175436779856682, accuracy=0.918828125\n",
            "Test loss = 0.24468101561069489, Test accuracy = 0.8947808146476746\n",
            "Step=42699 loss=0.21201423779129983, accuracy=0.916015625\n",
            "Test loss = 0.2635532021522522, Test accuracy = 0.8856009840965271\n",
            "Step=42799 loss=0.2148497149348259, accuracy=0.916953125\n",
            "Test loss = 0.262206107378006, Test accuracy = 0.8852840662002563\n",
            "Step=42899 loss=0.21443659529089928, accuracy=0.915546875\n",
            "Test loss = 0.25820115208625793, Test accuracy = 0.8875603079795837\n",
            "Step=42999 loss=0.21479082733392715, accuracy=0.9159375\n",
            "Test loss = 0.27821001410484314, Test accuracy = 0.8788818120956421\n",
            "Step=43099 loss=0.21349244989454746, accuracy=0.915078125\n",
            "Test loss = 0.2567519545555115, Test accuracy = 0.8886724710464478\n",
            "Step=43199 loss=0.2155441214889288, accuracy=0.912578125\n",
            "Test loss = 0.2489589899778366, Test accuracy = 0.893132746219635\n",
            "Step=43299 loss=0.2178712785243988, accuracy=0.914375\n",
            "Test loss = 0.26157617568969727, Test accuracy = 0.88663250207901\n",
            "Step=43399 loss=0.21761040076613425, accuracy=0.91703125\n",
            "Test loss = 0.2651369273662567, Test accuracy = 0.8845983147621155\n",
            "Step=43499 loss=0.21523003086447715, accuracy=0.91453125\n",
            "Test loss = 0.25752800703048706, Test accuracy = 0.8887185454368591\n",
            "Step=43599 loss=0.2159982641041279, accuracy=0.9165625\n",
            "Test loss = 0.25124412775039673, Test accuracy = 0.8913174867630005\n",
            "Step=43699 loss=0.21816875100135802, accuracy=0.913984375\n",
            "Test loss = 0.2663993537425995, Test accuracy = 0.88386070728302\n",
            "Step=43799 loss=0.2124902607500553, accuracy=0.9178125\n",
            "Test loss = 0.2571868598461151, Test accuracy = 0.8887819647789001\n",
            "Step=43899 loss=0.2176184944808483, accuracy=0.915546875\n",
            "Test loss = 0.28357428312301636, Test accuracy = 0.8766747713088989\n",
            "Step=43999 loss=0.21798163540661336, accuracy=0.916484375\n",
            "Test loss = 0.25329530239105225, Test accuracy = 0.8903378844261169\n",
            "Step=44099 loss=0.21834297105669975, accuracy=0.91671875\n",
            "Test loss = 0.2600395083427429, Test accuracy = 0.8875314593315125\n",
            "Step=44199 loss=0.2069932761788368, accuracy=0.920078125\n",
            "Test loss = 0.2604553997516632, Test accuracy = 0.8875372409820557\n",
            "Step=44299 loss=0.21918402910232543, accuracy=0.913828125\n",
            "Test loss = 0.25549009442329407, Test accuracy = 0.8890009522438049\n",
            "Step=44399 loss=0.21997873604297638, accuracy=0.912109375\n",
            "Test loss = 0.25362709164619446, Test accuracy = 0.8896463513374329\n",
            "Step=44499 loss=0.23433570191264153, accuracy=0.913984375\n",
            "Test loss = 0.25930583477020264, Test accuracy = 0.8920666575431824\n",
            "Step=44599 loss=0.21862167902290822, accuracy=0.914296875\n",
            "Test loss = 0.2665121257305145, Test accuracy = 0.8887243270874023\n",
            "Step=44699 loss=0.21019637927412987, accuracy=0.920546875\n",
            "Test loss = 0.26628613471984863, Test accuracy = 0.8874450325965881\n",
            "Step=44799 loss=0.22453297182917595, accuracy=0.914296875\n",
            "Test loss = 0.2566969394683838, Test accuracy = 0.8904703855514526\n",
            "Step=44899 loss=0.22351825147867202, accuracy=0.914296875\n",
            "Test loss = 0.25542137026786804, Test accuracy = 0.8908622860908508\n",
            "Step=44999 loss=0.21792735874652863, accuracy=0.91625\n",
            "Test loss = 0.2553868889808655, Test accuracy = 0.891086995601654\n",
            "Step=45099 loss=0.2110970875620842, accuracy=0.919609375\n",
            "Test loss = 0.2513396739959717, Test accuracy = 0.8927811980247498\n",
            "Step=45199 loss=0.2196316646784544, accuracy=0.91484375\n",
            "Test loss = 0.2557428479194641, Test accuracy = 0.8893005847930908\n",
            "Step=45299 loss=0.21940040804445743, accuracy=0.91578125\n",
            "Test loss = 0.269860178232193, Test accuracy = 0.8833132386207581\n",
            "Step=45399 loss=0.20913658775389193, accuracy=0.918203125\n",
            "Test loss = 0.26334914565086365, Test accuracy = 0.8870935440063477\n",
            "Step=45499 loss=0.21806341603398324, accuracy=0.91546875\n",
            "Test loss = 0.2534262537956238, Test accuracy = 0.8912368416786194\n",
            "Step=45599 loss=0.20905977800488473, accuracy=0.921171875\n",
            "Test loss = 0.25138330459594727, Test accuracy = 0.8922856450080872\n",
            "Step=45699 loss=0.21791928820312023, accuracy=0.915\n",
            "Test loss = 0.25555455684661865, Test accuracy = 0.8899978399276733\n",
            "Step=45799 loss=0.2137905715405941, accuracy=0.917265625\n",
            "Test loss = 0.26541826128959656, Test accuracy = 0.8852840662002563\n",
            "Step=45899 loss=0.22010720163583755, accuracy=0.911953125\n",
            "Test loss = 0.25144311785697937, Test accuracy = 0.8922567963600159\n",
            "Step=45999 loss=0.2095013852417469, accuracy=0.91984375\n",
            "Test loss = 0.2497953325510025, Test accuracy = 0.8926659226417542\n",
            "Step=46099 loss=0.2138818173110485, accuracy=0.91671875\n",
            "Test loss = 0.24773912131786346, Test accuracy = 0.8936801552772522\n",
            "Step=46199 loss=0.21113437965512274, accuracy=0.917109375\n",
            "Test loss = 0.24947960674762726, Test accuracy = 0.8930808305740356\n",
            "Step=46299 loss=0.2188681384921074, accuracy=0.913359375\n",
            "Test loss = 0.2726670801639557, Test accuracy = 0.8825526237487793\n",
            "Step=46399 loss=0.22087933465838433, accuracy=0.913359375\n",
            "Test loss = 0.2518419921398163, Test accuracy = 0.8919513821601868\n",
            "Step=46499 loss=0.21186235539615153, accuracy=0.916328125\n",
            "Test loss = 0.2579149603843689, Test accuracy = 0.889145016670227\n",
            "Step=46599 loss=0.21071181684732437, accuracy=0.919375\n",
            "Test loss = 0.2761344015598297, Test accuracy = 0.880904495716095\n",
            "Step=46699 loss=0.21181960456073284, accuracy=0.917890625\n",
            "Test loss = 0.2506963610649109, Test accuracy = 0.8930635452270508\n",
            "Step=46799 loss=0.2209308485686779, accuracy=0.914453125\n",
            "Test loss = 0.26497241854667664, Test accuracy = 0.8855606913566589\n",
            "Step=46899 loss=0.2077622213959694, accuracy=0.920625\n",
            "Test loss = 0.2785707414150238, Test accuracy = 0.8793889284133911\n",
            "Step=46999 loss=0.21372968278825283, accuracy=0.91953125\n",
            "Test loss = 0.2621302604675293, Test accuracy = 0.8874796032905579\n",
            "Step=47099 loss=0.22140879288315773, accuracy=0.913203125\n",
            "Test loss = 0.2571084797382355, Test accuracy = 0.8893582224845886\n",
            "Step=47199 loss=0.21059517361223698, accuracy=0.922890625\n",
            "Test loss = 0.26782554388046265, Test accuracy = 0.8837339282035828\n",
            "Step=47299 loss=0.2167738838493824, accuracy=0.917578125\n",
            "Test loss = 0.2532590627670288, Test accuracy = 0.8910409212112427\n",
            "Step=47399 loss=0.20655321545898914, accuracy=0.91921875\n",
            "Test loss = 0.24749040603637695, Test accuracy = 0.8941930532455444\n",
            "Step=47499 loss=0.20771913759410382, accuracy=0.920625\n",
            "Test loss = 0.25690510869026184, Test accuracy = 0.8905856609344482\n",
            "Step=47599 loss=0.21257638275623322, accuracy=0.9178125\n",
            "Test loss = 0.2543761134147644, Test accuracy = 0.8912022709846497\n",
            "Step=47699 loss=0.20255814887583257, accuracy=0.92140625\n",
            "Test loss = 0.2755475640296936, Test accuracy = 0.8817976713180542\n",
            "Step=47799 loss=0.21970332458615302, accuracy=0.91640625\n",
            "Test loss = 0.25544363260269165, Test accuracy = 0.8944696187973022\n",
            "Step=47899 loss=0.20931230798363687, accuracy=0.92046875\n",
            "Test loss = 0.27145400643348694, Test accuracy = 0.8864596486091614\n",
            "Step=47999 loss=0.2219851341843605, accuracy=0.915\n",
            "Test loss = 0.26652854681015015, Test accuracy = 0.8892083764076233\n",
            "Step=48099 loss=0.21273671671748162, accuracy=0.918359375\n",
            "Test loss = 0.25764963030815125, Test accuracy = 0.8922510147094727\n",
            "Step=48199 loss=0.22555860184133053, accuracy=0.914921875\n",
            "Test loss = 0.2588401138782501, Test accuracy = 0.8905049562454224\n",
            "Step=48299 loss=0.20246086187660695, accuracy=0.922890625\n",
            "Test loss = 0.254074364900589, Test accuracy = 0.8932768106460571\n",
            "Step=48399 loss=0.20463883869349955, accuracy=0.92125\n",
            "Test loss = 0.25955018401145935, Test accuracy = 0.8898595571517944\n",
            "Step=48499 loss=0.20972023859620095, accuracy=0.918515625\n",
            "Test loss = 0.2661145329475403, Test accuracy = 0.8871626853942871\n",
            "Step=48599 loss=0.21111091941595078, accuracy=0.91796875\n",
            "Test loss = 0.2685549259185791, Test accuracy = 0.8859755992889404\n",
            "Step=48699 loss=0.21219402611255644, accuracy=0.91796875\n",
            "Test loss = 0.2554128170013428, Test accuracy = 0.8920205235481262\n",
            "Step=48799 loss=0.20787816010415555, accuracy=0.9221875\n",
            "Test loss = 0.2595202922821045, Test accuracy = 0.8900036215782166\n",
            "Step=48899 loss=0.21016889899969102, accuracy=0.917890625\n",
            "Test loss = 0.2586193382740021, Test accuracy = 0.8903148174285889\n",
            "Step=48999 loss=0.21459564298391343, accuracy=0.914453125\n",
            "Test loss = 0.2479609251022339, Test accuracy = 0.895236074924469\n",
            "Step=49099 loss=0.2005949080735445, accuracy=0.92515625\n",
            "Test loss = 0.2561274468898773, Test accuracy = 0.8909083604812622\n",
            "Step=49199 loss=0.210956449508667, accuracy=0.91953125\n",
            "Test loss = 0.2524409592151642, Test accuracy = 0.8930751085281372\n",
            "Step=49299 loss=0.20561421662569046, accuracy=0.919140625\n",
            "Test loss = 0.2633521556854248, Test accuracy = 0.8873873949050903\n",
            "Step=49399 loss=0.2030230474472046, accuracy=0.920546875\n",
            "Test loss = 0.2668277621269226, Test accuracy = 0.8863328695297241\n",
            "Step=49499 loss=0.21346612617373467, accuracy=0.9175\n",
            "Test loss = 0.26995953917503357, Test accuracy = 0.8838952779769897\n",
            "Step=49599 loss=0.21015026934444905, accuracy=0.918515625\n",
            "Test loss = 0.2623288631439209, Test accuracy = 0.8882863521575928\n",
            "Step=49699 loss=0.21150056451559066, accuracy=0.919296875\n",
            "Test loss = 0.25572624802589417, Test accuracy = 0.8913002014160156\n",
            "Step=49799 loss=0.2014923672378063, accuracy=0.924609375\n",
            "Test loss = 0.2632320523262024, Test accuracy = 0.8885571956634521\n",
            "Step=49899 loss=0.2088476987928152, accuracy=0.917578125\n",
            "Test loss = 0.30071404576301575, Test accuracy = 0.8715978860855103\n",
            "Step=49999 loss=0.20582826010882854, accuracy=0.916015625\n",
            "Test loss = 0.2708054780960083, Test accuracy = 0.8842352628707886\n",
            "Reached 50001 epochs for CNN\n",
            "[[ 23978   6237]\n",
            " [ 13852 129466]]\n",
            "Normalized confusion matrix\n",
            "[[0.79357935 0.20642065]\n",
            " [0.0966522  0.9033478 ]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V0d7oeAN7dwUgbOF8o/KOvvVpUacqAEmJ8fx3SF+e9fMhsHUj9u7eYTjegX2/07tLWwLb\nNKZHgB9/RO4p0M8l331FrYr2hueJCdcZMagPga0b0cW/Kd/MnWnYdvL4UYK7deBZPx+Cu3Ug5kS0\nYduGtat4rm0zAls3YtTQ/txKTQHgixkf0aJuNaP2b924HoCL588ysHdXAlo15PkOvpyIPgxA3KWL\n1K1S1mift0cNA+DTD98zKm/b1Juez7YucmxLq4hdO2nVvAkN6tbiuc4BxMXFFaizb+9e/P18adSg\nDn4tmrLn998M21auWE7TRvVpWM+bvn16kZKSYti2bu3P1K9Tkzq1POjbpxepqalGx01PT8fbqzpT\nP5xsKNvwy3p8mzaiUYM6dGjbhuPHjhm2zZ0zm0YN6tCwnjcjXhlGTk6OYds3X31J7Zo1qF2zBqNG\nvIpOpwPA26s6np6e+NSvbXjcptPpGPf2WMpYaYz6nZuby7i3x+JTvza1PN2ZNXOGYdvZ2Fi6PNuR\n+nVq0qKJD4f//LPAeG3a+CtlrDRcvHABAKUUE0LH07CeNz71a/P+e+8W2OffjEXo+HeM+lPToxp+\nLZoWOGZppdFoivUo9dQT6vT19BJ5HD6boJxdKqift+5Rp6+nqwlTZ6h2nTrfd59VGyNUg0ZN1alr\naWryJ7NVkxatVPSFG+rQ6avKo6a3+nHtVnX6erpq066jCv1wujp9PV3tPnRKOTqVVb9G/KFOX09X\nwAO17+0JU9SQV0er09fTVZcXeqn/DB+jTl9PV7/s2q9cKriqqNjr6uj5JFXO2UWt2ZLfhwWLV6gK\nrm7q1LU0w3GuXr2qvOvUMzrv8y/2UcP++7o6fT1dRZ25pqp7eKlFK39Rp6+nK4+a3mr+ouXq9PV0\n9WXYSlWrdl11+nq6ijgYo8o7V1ARB2PU6evpasiro9VLQ15Vp6+nq1FjQ9WosaH37EfDxs3U+x/N\nVKevp6vvlq1V7jU81alraWrngROqcpVqDzQW/Qe9rCZM/azIsS2pR6ZOldgjKTlNubq6qn37D6lM\nnVKfzZqjunR9zqhOclqWcnFxUZu37VSZOqV+Xv+rerpSJZWpU+rU2YuqQoUK6tTZiypTp9SY199U\nr44YqTJ1Sp08fU5VrFhRHTt5RmXk6NXI0a+pRT/8aHTs1998S1Vzd1fvvT9JZeqUir0Qp8qVK6ei\njhxXmTqlZn8xX7Vs5acydUpF/B6p3KtXV9cSb6qMHL3q8WIv9fH0GSpTp9T2Xb8rTy8vdelqgrqV\nqVN9+vZTW7bvUpk6paq5u6vz58/fs/+Bnbuo0AkTFaDOnL9sKJ8zd4Fq5dda3byVqa4nJSvv2rXV\ntl2/qUydUs2at1CzZs9VmTql1v+6WXl6eamMHL1h3xsp6apevfrK2dlZxZzJP2/Y0mWqeQtflZyW\npVLSs1UL35bqx+WrHnos/vl45dURaubnX5TotfIoVRm5tliPB3Hq1CnVsWNHtWTJEqWUUlevXlUh\nISGqX79+asyYMSo7O1sppdS6devUiy++qHr37q1WrlyplFIqJydHvfnmm6pv377qpZdeUpcuXVJK\nKXXy5EkVHBysgoOD1cSJEw3nWrhwoerVq5fq3bu3ioiIKLJtkqH+S/+3dzdV3WtQr2FjAHr1G8je\n3TtIS7tV6D5TJ7zN+MnT0Gg07P1tJ8/37IONrS2OTmXpFRzC1l/XAhA8YChBLw0G4OnKVXCv7sH5\nc7EP3LakxHiWhX3LyDfHA7Dvt5307jcQAO869anXsDGRv0eg0+Xw8awF1PfJ70Mr/3YkJSaQmpJs\nONZrr73GiNfHGR3/9MnjtPJvB4CDoxP1fRpzJuYEp04e41ZKMgFdngegY+Bz3EhKJPZ0DNs3b6CV\nfzsqVcnPmoP6D2LzL2vu24+0W6kc/fMgvfoOAMC/fQCWllacPH70gcfi9MnjHIjcQ//BLwPFH9v/\nNRG7dlK9hgeNmzQBYNCQ/7B921Zu3bpzHep0OuZ9+Q1t27UHwK91G65dvUpycjIb1q+jXYeOVKtW\nDYDBQ4by8+pVACwLX0r3nr3w9PJCo9Hw2azZ9O3X33Dc6KNHidi5g379QwxlVlZWhC1dRp26dQ3n\nOnniOABrVq+id1Aw5cqVQ6PRMGjwf1jzU/65loQtYujLr+Lq6oqlpSVhS8J5pm27Ivs/PvR93p/0\nQYHyHTu2Edy3P7a2tpQtW5YBg4awds1qUlNTOfjHAQYOHgJAwLOBWFlZcfTIEcO+Uz+cTL+QATg4\nOhrK1qxexYCBg7GxscHa2pr+Lw1gzd/j9DBjcbfjx47x+++7eWX4iCL7K/JlZGQwZcoUWrVqZSj7\n4osv6N+/P+Hh4bi7u/PTTz+RkZHB/PnzWbx4MUuWLCEsLCz/ut+wAScnJ5YtW8bw4cOZOTN/lu2j\njz4iNDSU5cuXk5aWxu7du7l8+TIbN24kPDycr7/+mmnTppGXl3ff9klA/ZfOnz1Dteo1DM/t7R0o\nV96ZS+fP3rP+rm2bsbUtQ/OW+VOPGo3G6IdiZ+/AxfPnAAh8rjv29g5A/rRxQsJ1mrW4c+G8NfI/\ndPFvSv8ezxL1x/8VONd3C+bQMzgEp7Ll7pxLf+dc9vYOXDp/FkensnTq3A3In9L6KTyMZi1bU7Zc\neQB279hCamoqXbv3Mjp+K/92bFq/htzcXOKvX+Pon4fwbf0MF87GUtW9hlHdqu41OBd7mgvnYo3G\nq1p1D24kJZKSfBOAfb/vIrhbBwJbN2LapPHkZGfD31M/eqW/a5zsufT3OKWl3WLE4GAC2zRmaL/u\nxJ6OKTAW82ZOY9jI17G0tHygsS1tzpw5jYeHp+G5g4MDLi4unI2NNSrr0fNFw/MtmzdRs1YtypUr\nV2B/D09PEhISuHnzJkePHsHa2prnOgfQoG4tRv93OBkZGUD+9TJm1AhmzZlnGFsANzc3ng3sfOdc\nWzbRvIUvALH/PJeHJ6dP5f/Moo8eIT0tjY7t/GlYz5uJE0KNfj/efvttmjVqQOuWzdnwy3pDectW\n9/7Z/fP3y8HegXNnYw3TiXr9nWvK3t6Bs2fzx+tYdDQ7t29jzGtvGB0v9vRpPDyNx+l0TMxDj8Xd\nPpr6AW+Ofcdo39KupKd8ra2tWbhwIW5uboay/fv307FjRwDat29PZGQkR44coUGDBjg6OmJra0uT\nJk2IiooiMjKSgIAAAPz8/IiKiiInJ4crV67QsGFDo2Ps378ff39/rK2tcXZ2pnLlysTG3v9NuATU\nfykrMxMbG1ujMhvbMoYXnH/6dv7n/GfEa4bnrZ/pwE/LwkhNSebmXzdY99MysrOzDNuvxl2mQ/O6\nvPzSi7z/0UycK7gC0CdkCMNGvsGm3w8RMuRVhg8MMsoob6WmsHZVOC8NecVQ5vdMB8K+mU9eXh4x\nJ6KJ3Lub7Oxsw/bNv/xM64YehId9y4fT5xj698nkUObPn1+gL6Pfeo/ow4doUacq7Zp6E9itB3Xq\nNSQzMwNrGxujura2tmRmpJOVmWE0XtY2Nmg0GjIzMqjXsBEBXZ5nyepNrNiwk6OHD/HNvFk4ODji\n06Q5i76ai1KKvb/t5MypE2RnZ2Hv4EC3nn1478NP2fTbIfye6cB/BweTm5trOMfF82c5HHWA518M\nNmpTYWNbGmVmZGBra3wd2pYpQ3p6+j3rRx89yjtvvcG8BV/fc3+bv38u6enppCQns3P7Nhb98CP/\n98efnDt3lk8/+RiAb7/5mjp16tLKz6/Qtu3auYN5cz7n08/y1wdk/ONcd7czOSWZfXv3sPaXjezc\nvZdNv27gh8WLAAjq05eRI0dy8HA00z+bxX8GhRi9YbiXjh0DCFv0HcnJydy4cYPwH5eQlZWFo6Mj\nzVv48sXsWSil2LljOyeOHyM7KwulFKNHDmfm7LlYWVkZHS8jMwObu9pepkwZ0jPSH3osbjsbG8uB\n/f9H8F2Z/5OgpAOqpaVlges+MzMTa2trAFxcXEhMTCQpKQlnZ2dDHWdn5wLlWq0WjUZDUlISTk5O\nhrpFHeN+SiygxsXF0bhxYwYMGEBISAiDBg0iMjKypE73yJSxszMKgABZmRnY2dsXqHv96hXOnDrB\nMx0CDGVBLw2m9TMdCOrajtFDX6L1Mx1wcipn2F6pSlV2/nGCn7fuZda0yURs3wzA1M/mUbtuAwC6\ndu/FUxWfNspSd23bhE+T5ji7VDCUvT91BqmpKXTxb8KXs2fwTPtOODqVNWzv/HxP9kWfZ/InnzOg\nVxcSE64zb9Y0XugVjOdd78pvG//6cJ59rgeHTl8l8tgF/m/PbjauW00ZO/v8zPIumZmZ2Nk7UMbO\n3mi8br+A2dnb0zHwOYaOeA1rGxvKlXdm8Cuj2LVtEwAzF3xP1B+RBLZuxKb1a2jaohVOTuUo7+zC\npGmzqFLNHa1Wy3+GjyEpMYELZ88YzrFx3WoCurxQ4MWxsLEtjezs7MnKMr4OMzMycHBwKFA3ct8+\nerzQlS+//tYwnWpnb7x/1t8/FwcHB5zKluX57j1wc3PD3t6el18dwfZtW4mPj2fuF58zddr0Qtu1\nft1aXh46mNVrNximPO3/ca6721nWqSx9gvvh6OhIhQoVCBk4mO3btwIw9eNPaNcuv71t2vjzTNt2\nbN+29b7jMmToMDp0CuCZ1r7069OLjp0CKFsu//dr8Q8/ErlvLw3rebN61Ur8WrehbLlyfLfwG+rU\nqUvrNm0KHM/ezp7su9qekZGBg73DQ4/FbT+tWsEL3XsWuEZLu8e9KEkpVezyf3uMu5VohlqjRg2W\nLFnC0qVLmTJlClOmTCEmpuD0XGni4VXLMEUL+ZlhSkoy1Wt4FagbsX0zfs90wMLCwlBmaWnJuEkf\ns2XvYZb+vBkLS0tq1alHTnY2q8LDDNNVVd2r065jIHt37yA9PY1zsaeNjp2bm2f0y7hr2ybadgw0\nquPi6sa878LZuu8Ic775gYTr1/CuU49rV+LYtukXQ71WbdpRsVJlDh/6g51bNvLDtwuoWLEifg3y\np2r9GtTg4vmz7I3YwfMv9kGj0VCuvDNt2nXkQOQePL1qcenCnTFRSnHxwjm8atUuMF4Xzsfi9lRF\nnMqW4+L5s6TdurN6NC8319CnatU9WLTiF7buO8LUz+Zx6cJ5atWpR0ryTS5fvGDUT31eHpYFxuJZ\nw/P7jW1p5V27tmG6EiAlJYWbN2/iVbOmUb3oo0d5qV8QYUuX0blL1zv7exvvH3vmDBWffppy5cpR\nrZq70YpfCwsLLCws2LljO4kJCTRpWJfqVSoye9ZnzJ71GaP/OxyAnTu289abr7Fh41aaNmtm2L/W\nP88Ve4badfIDTFX3e58rOzubE8eN7zvm3nV9FMbS0pJp02dw9Pgptu6IwNLSkvr189+Ienh6smHT\nVqJPnGb+V99w7txZ6tVvwIZf1rHhl3VUr1KR6lUqEnf5Mm1aNWd3xC5q1a5tlBXHnjlD7bp1H3os\nbtv46wajn8eT4nEEVDs7O8Mbtvj4eNzc3HBzcyMpKclQJyEhwVB+O8vU6XQopXB1dSU5+c5sX2HH\nuF1+P49syrdatWoMHz6c8PBwwsLCCA4OJjg4mG+++YaLFy8ybFj+Rx+ioqJo1qwZer2e3NxcunXr\nxpo1a3jvvfcYMWIEnTt3ZtWqVUWcreS0bN2Wq3GXOLh/HwCLvp5H+4Au98xQY45H41nT26hs/erl\nvP7qIPR6PfHXr7FmxVJe6BWMtY0NX82ZwdqVPwKQnp7Ggcjf8a5Tn2tX4gju1oGLf9+n3ROxnZt/\nJeHTpPld5zpW4FwfvPsmi76eC8D+vb8Rf/0aTX390OlyGP/aq5yJOQHAhXOxXDx/jpreddj420Ei\nj13g+vXr7Is+D8C+6PO41/CkhldNdm7dCORPDf/fnt3Uql0XL+86OLtU4Jc1KwD4ecVSKlepSg3P\nmnTs3I3IPRGGNwSLvprLcz2CAJjz6RRmfjwZpRTZWVksX/IdbTvl33saPjCILRvyF2utXRVOpcpV\nqVy1GtGHDzGod1f+Ssr/pVixdBFPV65qdA/31IljeNa88xGL+41tadW2XXsuX7rI3j35H3eaO+dz\nujzXDfu7rkOlFC//ZxBz5i6gTRt/o/27vdCdiJ07OH3qFABfzJ5Fn+B+APTq3YfVq1YQFxdHXl4e\nYYu+o0PHTvTr/xLXEm9yIe46F+Ku8/qbb/H6m28xd8FXZGRk8MqwISxfuYbadeoYnatX7z6sXLGM\n+Ph4cnNzmT93juFcvYOCWfSWvOZFAAAgAElEQVT9QlJSUsjMzGRZ+FI6dOhERkYG7fxbGWa1jkVH\nE7lvLx06drrvuCwL/5EBL/VFr9dz9epVlvywmL79Xso/V88X+HnNagDCly6hatVquLu7s/aXjVy6\nmmDoV5WqVdkT+Qdt27WnV+8+fP/tN6Snp5OWlsb3335Dn+B+Dz0Wtx2LPop37XtvE/+On58fW7Zs\nAWDr1q34+/vj4+NDdHQ0qamppKenG+JK69at2bw5f2Zq165d+Pr6YmVlhYeHBwcPHjQ6RsuWLYmI\niCAnJ4f4+HgSEhLw8iqYOBl5oHXKD+Hy5cuqZ8+eRmVnz55VzzzzjOrevbvS6XRKp9OpHj16qIsX\nL6pu3bopvV6vFi5cqEJCQlRMTIw6evSoGj9+vFq9erXq3bu3ys3NVbGxseqFF14o8vxZuryS6pra\ntWuXatiwofL09FSBgYHq2rVrKi4uTtWrV8+o3vPPP6+++uoro7K0tDTVs2dPVb16deXl5aVWrFhh\n2Hb48GHl7++vvL29laenp3rjjTdUXl5+P8LCwlSdOnWUt7e3atmypdq3b5/RcR0dHVVMTIxR2cmT\nJ1XTpk2Vh4eHatq0qTp69Khh28qVK1WDBg2Ut7e3ql27tlq0aNE9+3r3JRIdHa3atm2ratasqby8\nvNSoUaOUTpe/Lv/o0aPK19dXeXl5qdatW6uTJ08a9luxYoWqXbu28vLyUn369FG3bt1SSikVHx+v\nunfvrry8vJS3t7d64403VFZWllJKqT179qi6deuqGjVqqHbt2hmWtyul1Keffqpq1qypvL29VYcO\nHdSJEycM227cuKEAw3EeZGxLq6Kuw3379imtVqu8vb2NHocOHVJKFf5zUUqpBQsWKHd3d+Xl5aWG\nDBmi0tLSCpx/0qRJatKkSUoppcLDw5WNjU2Bc12/fl0ppdScOXMM182IESMM141SSr3//vuqSpUq\nqmbNmuqtt95Subm5SimlNm3apBo2bKhq1aqlGjVqpNavX6+UUur69euG4wPK09NTeXt7q7i4uPv+\nft3vmrqb+98f17lt/PjxysvLS9WsWdPQ3+KMRWHX6JOg+usbivUoSnR0tAoJCVHt27dXAQEBKiQk\nRF2/fl0NHjxY9evXT40dO1bl5OQopfKvn969e6ugoCC1bt06pZRSubm5avz48apv375q4MCB6urV\nq0oppc6cOaP69eungoOD1ccff2w43w8//KCCgoJUnz59Crzm3otGqQeYGH4IcXFxjBkzhjVr7nxE\nIjo6mtDQUJo0acIHH+QveZ8yZQq+vr5s2bKFkSNHMnv2bLp06UJqaipZWVmULVsWvV7PyZMnee+9\n90hPT+f5559n586d9z3/mfh7LxISD6bmU3YyhsVU1cXucTeh1LO1hKzcouuJwtk+wkXEHm9uLNb+\n52aV7mnwR7rK99ixY6Snpxvd3NXpdGi1Wlq0aMGRI0fIysrC19eXw4cPExUVha9v/nLzJ2lpuRBC\nPIke96Kkx+2RBdRLly6xePFipk+fzuHDh8nNzSU3N5cjR45Qp04dmjdvzrp166hWrRrOzs7cvHmT\nv/76i6effvpRNVEIIUQxaDTFe5R2JZr2nT9/ngEDBpCTk0NeXh4TJ06kefPmBAcHExISglKKoKAg\nKleuDEBsbCxBQfkLVpycnKhQocL9Di+EEEL8zyixe6iPm9z/Kx65h1p8cg+1+OQeavE9ynuoNd8u\n3me7z8zoXHSl/2FyY1IIIYRJPAnTtsUhAVUIIYRJPAkLi4pDvstXCCGEMAHJUIUQQpiEmSeoElCF\nEEKYhlZr3hFVAqoQQgiTkAxVCCGEMAFZlCSEEEKIYpMMVQghhEmYeYIqAVUIIYRpmPuUrwRUIYQQ\nJiEBVQghhDABM4+nsihJCCGEMAXJUIUQQpiETPkKIYQQJmDm8VQCqhBCCNOQDFUIIYQwATOPp7Io\nSQghhDAFyVCFEEKYhEz5CiGEECZg5vFUAqoQQgjTkAxVCCGEMAEzj6eyKEkIIYQwBclQhRBCmIRM\n+QohhBAmYObxVAKqEEII0zD3DFXuoQohhBAmIBmqEEIIkzDzBFUCqhBCCNMw9ylfCahCCCFMQgKq\nEEIIYQJmHk9lUZIQQghhCpKhCiGEMAmZ8hVCCCFMwMzjqQRUIYQQpiEZqhBCCGECZh5PZVGSEEII\nYQqSoQohhDAJrZmnqBJQhRBCmISZx1MJqEIIIUxDFiUJIYQQJqAt4Xianp7OuHHjSElJQafTMXLk\nSFxdXZk8eTIA3t7efPDBBwB8++23bN68GY1Gw6hRo2jbti23bt1i7Nix3Lp1Czs7O2bOnEm5cuXY\nt28fs2bNwsLCgmeeeYaRI0c+VPskoAohhCgVfv75Z2rUqMHYsWOJj49n0KBBuLq6EhoaSsOGDRk7\ndiy7d+/Gw8ODjRs3snz5ctLS0ujfvz9t2rQhLCyMFi1aMGzYMFasWMHChQt5++23mTp1Kt999x1P\nPfUUISEhBAYG4uXl9a/bJ6t8hRBCmIRGoynWoyjly5cnOTkZgNTUVMqVK8eVK1do2LAhAO3btycy\nMpL9+/fj7++PtbU1zs7OVK5cmdjYWCIjIwkICDCqe/nyZcqWLcvTTz+NVqulbdu2REZGPlT/JaAK\nIYQwCY2meI+iPPfcc1y9epWAgABCQkJ45513cHJyMmx3cXEhMTGRpKQknJ2dDeXOzs4Fyl1cXEhI\nSCAxMfGedR+GTPkKIYQwCQ0lexN13bp1VKpUie+++46YmBhGjhyJo6OjYbtS6p773au8sLrFIQFV\nCCGESZT0oqSoqCjatGkDQO3atcnOziY3N9ewPT4+Hjc3N9zc3Dh//vw9yxMTE3F0dDQqS0pKKlD3\nYciUrxBCiFLB3d2dI0eOAHDlyhXs7e3x9PTk4MGDAGzduhV/f39atmxJREQEOTk5xMfHk5CQgJeX\nF61bt2bz5s1GdatUqUJaWhpxcXHk5uaya9cuWrdu/VDtkwxVCCGESZT051CDg4MJDQ0lJCSE3Nxc\nJk+ejKurKxMnTkSv1+Pj44Ofnx8Affr0ISQkBI1Gw+TJk9FqtQwYMIC3336b/v374+TkxIwZMwCY\nPHkyY8eOBaBr167UqFHjodqnUSUxkfw/4Ex8xuNuQqlW8yk7GcNiqupi97ibUOrZWkJWbtH1ROFs\nH2Ha1OPbg8Xaf+2wZiZqyeMhGaoQQgiTkO/yFUIIIUzAzOOpLEoSQgghTKHQDPWnn3667469e/c2\neWOEEEKUXvLl+IU4dOjQfXeUgCqEEOJuZh5PCw+o06ZNM/xbr9dz48YNXF1dH0mjhBBClD7mviip\nyHuokZGRdOrUiQEDBgDw8ccfExERUdLtEkIIIUqVIgPq559/zsqVKw3Z6fDhw1mwYEGJN0wIIUTp\noinmo7Qr8mMzdnZ2VKhQwfDc2dkZKyurEm2UEEKI0kcWJRXB1taWAwcOAJCSksKvv/6KjY1NiTdM\nCCFE6VLSX47/v67IKd9Jkybx3XffER0dTUBAAL///jsffvjho2ibEEKIUqSk/8D4/7oiM9Snn36a\nr7/++lG0RQghhCi1isxQ//jjD3r16kWjRo1o3LgxwcHBRX5GVQghhPnRaIr3KO2KzFA//PBDQkND\nadKkCUopDh06xAcffMD69esfRfuEEEKUEk/CtG1xFBlQXVxcaNWqleF569atqVSpUok2SgghROlj\n7ouSCg2oly9fBqBBgwZ8//33+Pn5odVqiYyMpG7duo+sgUIIIUoHyVALMWjQIDQaDbf//vjSpUsN\n2zQaDWPGjCn51gkhhBClRKEBdefOnYXuFBUVVSKNEUIIUXqZd376APdQ09LSWLduHTdv3gRAp9Ox\nevVq9uzZU+KNE0IIUXrIl+MX4fXXX+fUqVOsWbOG9PR0du3axeTJkx9B04QQQpQm5v6xmSIDanZ2\nNh9++CGVK1dm3Lhx/PDDD2zatOlRtE0IIUQpYu7flFRkQNXpdGRkZKDX67l58yblypUzrAAWQggh\nRL4i76F2796dlStXEhQURNeuXXF2dqZatWqPom1CCCFKkScgySyWIgNqv379DP9u1aoVN27ckM+h\nCiGEKMDcFyUVGlDnzJlT6E7btm3jtddeK5EGCSGEKJ3MPJ4WHlAtLCweZTuEEEKUck/CwqLiKDSg\njho16lG2w+TK21s/7iaUejKGxVO+een+HfpfkPnnPBnHYsr8c97jboLZKPIeqhBCCPEgivzYyBNO\nAqoQQgiTMPcp3wd6Q3Hz5k2io6MB0Ov1JdogIYQQpZNWU7xHaVdkQN2wYQPBwcG8++67AEyZMoVV\nq1aVeMOEEEKULhJQi7Bo0SLWrVtH+fLlARg3bhwrV64s8YYJIYQQpUmR91AdHR0pU6aM4bmtrS1W\nVlYl2ighhBClj7nfQy0yoJYvX56ff/6Z7Oxsjh8/zsaNG3F2dn4UbRNCCFGKPAnTtsVR5JTvBx98\nQHR0NOnp6UyYMIHs7GymTp36KNomhBCiFDH3P99WZIbq5OTExIkTH0VbhBBCiFKryIDatm3be86L\nR0RElER7hBBClFLy5fhFCA8PN/xbp9MRGRlJdnZ2iTZKCCFE6SPflFSEypUrGz2vXr06Q4cOZfDg\nwSXVJiGEEKWQmSeoRQfUyMhIo+fXr1/n0qVLJdYgIYQQpZNM+RZhwYIFhn9rNBocHBz44IMPSrRR\nQgghRGlTZEAdP3489erVexRtEUIIUYqZeYJa9D3k6dOnP4p2CCGEKOXM/bt8i8xQK1WqxIABA/Dx\n8TH6ysHXXnutRBsmhBCidHkU91DXr1/Pt99+i6WlJWPGjMHb25t33nmHvLw8XF1dmTFjBtbW1qxf\nv56wsDC0Wi19+vQhKCgInU7H+PHjuXr1KhYWFkybNo2qVasSExPD5MmTAfD29n7o25pFZqhVqlTB\n19cXW1tbLCwsDA8hhBDibiX9TUk3b95k/vz5hIeH89VXX7Fjxw6++OIL+vfvT3h4OO7u7vz0009k\nZGQwf/58Fi9ezJIlSwgLCyM5OZkNGzbg5OTEsmXLGD58ODNnzgTgo48+IjQ0lOXLl5OWlsbu3bsf\nqv+FZqjr16/nhRdeYNSoUQ91YCGEEMKUIiMjadWqFQ4ODjg4ODBlyhQ6dOhgyCjbt2/P999/T40a\nNWjQoAGOjo4ANGnShKioKCIjI+nRowcAfn5+hIaGkpOTw5UrV2jYsKHhGJGRkbRt2/Zft6/QDPWn\nn3761wcTQghhvkr6HmpcXBxZWVkMHz6c/v37ExkZSWZmJtbW1gC4uLiQmJhIUlKS0R9xcXZ2LlCu\n1WrRaDQkJSXh5ORkqHv7GA+jyHuoQgghxIPQUPL3UJOTk5k3bx5Xr15l4MCBKKUM2+7+993+TXlh\ndR9EoQH1zz//pF27dvc8mUajke/yFUIIYaSkV+q6uLjQuHFjLC0tqVatGvb29lhYWJCVlYWtrS3x\n8fG4ubnh5uZGUlKSYb+EhAQaNWqEm5sbiYmJ1K5dG51Oh1IKV1dXkpOTDXVvH+NhFBpQ69aty6xZ\nsx7qoEIIIcxPSQfUNm3aMH78eF5++WVSUlLIyMigTZs2bNmyhe7du7N161b8/f3x8fFhwoQJpKam\nYmFhQVRUFKGhoaSlpbF582b8/f3ZtWsXvr6+WFlZ4eHhwcGDB2nWrBlbt25lwIABD9W+QgOqtbV1\nge/xFUIIIR6Xp556isDAQPr06QPAhAkTaNCgAePGjWPFihVUqlSJHj16YGVlxdixYxk6dCgajYaR\nI0fi6OhI165d2bdvH/369cPa2ppPPvkEgNDQUCZOnIher8fHxwc/P7+Hap9GFTJhPGPGDN5+++2H\n7Pbjl5SW+7ibUKpVcLCUMSymqv6vP+4mlHqZf86jTGP5pEFxZP4575Gda0bEuWLt/3Y7DxO15PEo\nNEMtzcFUCCHEo/ckfNtRccgqXyGEECZh7t/lKwFVCCGESZj7n28z9z+wLoQQQpiEZKhCCCFMQu6h\nCiGEECZg5jO+ElCFEEKYhvYRfPXg/zK5hyqEEEKYgGSoQgghTEKmfIUQQggTkEVJQgghhAmY++dQ\nJaAKIYQwCTOPp7IoSQghhDAFyVCFEEKYhEz5CiGEECZg5vFUAqoQQgjTMPd7iBJQhRBCmITGzFNU\nc39DIYQQQpiEZKhCCCFMwrzzUwmoQgghTERW+QohhBAmYN7hVAKqEEIIEzHzBFUWJQkhhBCmIBmq\nEEIIkzD3j81IQBVCCGES5j7lKQFVCCGESUiGKoQQQpiAeYdTydCFEEIIk5AMVQghhEnIlK8QQghh\nAuY+5SkBVQghhElIhiqEEEKYgHmHU8nQhRBCCJOQDFUIIYRJmPmMrwRUIYQQpqE180lfCahCCCFM\nwtwzVLmHKoQQQpiAZKhCCCFMQiNTvkIIIUTxmfuUrwRUIYQQJiGLkoQQQggTMPcMVRYlCSGEECYg\nGaoQQgiTMPcMVQKqEEIIk5BVvkIIIYQJaB9RPM3KyqJbt27897//pVWrVrzzzjvk5eXh6urKjBkz\nsLa2Zv369YSFhaHVaunTpw9BQUHodDrGjx/P1atXsbCwYNq0aVStWpWYmBgmT54MgLe3Nx988MFD\ntUvuoQohhDAJTTH/e1BffvklZcuWBeCLL76gf//+hIeH4+7uzk8//URGRgbz589n8eLFLFmyhLCw\nMJKTk9mwYQNOTk4sW7aM4cOHM3PmTAA++ugjQkNDWb58OWlpaezevfuh+i8BtZh+372LDm2a49uo\nLr1f6MzVK3EF6hyLPkLXjv74NqpL147+HD921LDti1kzaN2sIY3qePD++LdQShm2Lfr2K5rWr0nT\n+jUZO2YEOp0OgNzcXN5+fSQ+tWvQ3Kc23y/8EoBLFy/wdPkytGpS3/AY+cpgw/FmTv+IVk3q49uo\nLsMG9Sc1JQWAW6mpjBg20LDtk6mTDfvEXb5E7xc649uoLh3aNGfPbxEP1Pev5s/Br2kDWjaux+sj\nXyEnJweAnJwcXh/5Cr6N6uLXtAHffDnXsM/pmJN079IRv6YNeMa3ERvW/QzA+rWrjfrUqkl9XB2t\nSLt1i7S0NEYPH0rLxvVo1qAWq5b/aDT2K5ctxb1iuQLlT5q8W3Fkn1pB9sml5MSuQ+WkFayTepHs\nmOVkHf+BnHMbULlZhm25CUfIPhlO9skf0V3aidLnAaDyssm5sIXskz+SfXIpumv7DfvoM2+Qfebn\n/P1ilpGXfLbgOVMukHV4PvrsVAByLm79+1j5j6xji8g5vwmVc8uoPPvkj2Qd+Zpffvklv33xUWSf\nDCfreBi6K3sMvydKl0HO+Y1kn1xK9skfybt1ucj+KqVHd2VP/jmOh5GbEGUoL9CGowvJTTz6d1/O\nkx2zPL+/Z9agz7xR9Pjdp33i4Zw9e5bY2FjatWsHwP79++nYsSMA7du3JzIykiNHjtCgQQMcHR2x\ntbWlSZMmREVFERkZSUBAAAB+fn5ERUWRk5PDlStXaNiwodExHoYE1GJIT0/nlcEv8fm8r9l/+ATP\ndnmOt14bWaDeK4NDGPX6W+w/fIIxb77DiKEDAdi+dTNLf/ieX7ft5sCRGI4cjjK88P/fvj18OW8O\nW3btY//hk6SlpXHg//YB8MXnM0hMSCDqeCwbt//GmlUruPnXXwA8XakykVHHDI/53ywG8oPSujWr\n2LY7ksioY2g0GubO/gyAqR9MwMrKmr0Hj7Lj9/2sXrmMbdu2AfDmmBF0CuzC/sMnmLNgIa8MCSEz\nM/O+fT944P9Y+OU8Nu34ncioY6SkpLDwy3kAfDl3Nsk3bxIZdYwtu/byzYK5HI46CMDQgf0I7j+A\nfYei+er7JYx8dQipKSm80KOXUZ/GTZjEcy/0xMHRkVnTPyIjI4N9h6JZv3knH7z/LhcvnAdgzsxP\nWf/zarxq1jL5z/5/icrTobu4FauqHbCpE4K2bHV0cRHGdXIz0V3chlW1jtjWG4jG1oXcq/nXkz79\nOnlJR7Cu2Qvr2v1ReTnkJeUHkdyr+9BY2WNT5yWsawahv3mavNQLAOgubMbS1QebOv2xqtYJ3aXt\nRkFa6XXkXosECxtDmbX7s9jUecnw0JapgIVzbTTWjkbl1p7Po7G2p1OnTuSlXiTvxgmsa76ITZ0Q\n9BmJ6G+ezm/Dld/RWJfFpk4IVtU7o7u4DZWXc9/+5t04gT4jAWvvYGxq9yXvRgz6tKtoNFrjNtQK\nQmNpi0U5T1ROGrpLO7Cq/iw2dfpjUb6mYYzvN36Fte9JpdEU7/Egpk+fzvjx4w3PMzMzsba2BsDF\nxYXExESSkpJwdnY21HF2di5QrtVq0Wg0JCUl4eTkZKh7+xgPQwJqMezZvQv36jXwadQEgP4DhhCx\ncxtpt24Z6pw4Hk1KSjJdn+8OQOfnnicpMZHTMSfZvXM7z3XrTrny5bG2tuY/r4wwZGXLloYxaMjL\nVHB1xdLSkq+/X0Jr/7b525Ys5vW3xmFhYYGrqxsbtkZQ/q6L515qeddm7lff4eDoiFarpblvS06d\nPAFAtxd6Mu69SWi1WhwcHalXvyHHjx8nNSWFPbt3MWDwMAAaNGxElapV2fv77vv2ff3Pq+n+YhBl\ny5VDo9HQf8Bg1q/9CYD1a39iwJBhaLVaHJ2ceL77i6z/eTV5eXm8OS6UPv1CAKhbrwHW1tZcvHje\nqB9ZWVlM+3ASk6ZMAyBi13b6vjQQrVZLpcpV6NrtBTb9uh6ANs+0ZcmKNTg4OD7Mj7fU0KfFobF2\nQmvnCoCFcx30ty4bvXDr06+jsSlrqGPp6mPIKPOSY9GWq4nG0gaNRoOFS23ykmMB0Jb1xNKtMUD+\n9jKuqKxklMrDsmILtGVr5NezcwWNJSrnzrWfe/0PLMp7g9b6nu3OS70IKg+Lv49xN93VSCyfak6Z\nMmXQ37qMtpwHGktbNFoLLCvUN7Rdf+syFi518ttQxgVtGVf0t+Lu21/9rctYlK+JRmuJxsIGC+fa\n98yuc+MP5gd7K3vQaLFyD0Br+/eLsf3TqMy/ihy/wtr3pCrpKd+1a9fSqFEjqlates/td8/wPWx5\nYXUfhATUYjgbe4bqHh6G5w4ODpR3duHcuVijOu7VjV8w3KvX4MzpU2g0GvLy8gzl9vYOnD+X/4t9\n/NhR0tPT6PZsO1o2rsfUyRPIy8sjLS2NC+fPEXXoD9r5NaVdqyasXrnMcIxbt1IZ2LcXrZrUp0/P\n5zgdcxKA2nXq4dO4qaHejm1baNKsBQD+bdtTuUr+BXorNZUD+yPx9fXl/LlYXCq4Ym9vb9iveg1P\nYk/H3LfvZ2PPUKPGnW01anhw5vSpO2N217bqHvnbLCws6NmrD5aW+evkDv2RP7Xo6WWcXf74w/e0\naNmKGh6eAGg0GvSFjGHT5r5ozGAdv8pORmNz5x22xsIaLGxR2Sn/qHjXC4XWCvT5mZzKTjHe37os\nKisZAAunavkBBdBnJaPPiEfrWBWNxiI/KP09vnnJ59BY2KD5O+DoM2/kBxM3n0LbnXv9AJYVmxco\n12feQGUkoi1/189e6e/828IKlXNX3+7u193bCunvffe5fcjcTPL+OoWFa377NVZ2WDi532lj6iW0\n9k/l173P+D3IuZ4kWk3xHkWJiIhgx44d9OnTh1WrVrFgwQLs7OzIysqfGYmPj8fNzQ03NzeSkpIM\n+yUkJBjKb2efOp0OpRSurq4kJ9/5ed0+xkP1/6H2EgBkZmZgY2NrVFamTBky0tPv1MnIwNbWuI5t\nmTJkZKTTtkMn1q5ZxdUrcWRkZLB08XdkZedfGCkpKeyP3Mvy1b/w67bdbNv8K+FLFpOakv+Dj7t8\nmZ17/mDu19/z1usjOXMqBgcHR14M6svU6TPZe/Ao7dp3YkDfXuTm5hqdf9aMaSQmxPPyiFFG5Tk5\nObz6nwF07tqNVq1akZGRWbDttrakZ2Tct++ZmRnY3LWf7V1j8s/xsLXNH4u7XYm7zPChA5k2YzZ2\ndnaGcr1ez5dzZzPytTcNZW3bd+K7hV+SlZVF3OVL/LphHdlZWZgVfS4ajfGCfY3WEvQ6w3OtfUVU\ndjJ5ty6jlCIv8TCgBX0e6HVG+/9zX6X0ZJ9YQs7pFVi6NUFbxuXOqdOv/31f8zesqnVAo7VAKYUu\nLgKryv5oNBb3bHLerThQoHWoXGBbbsKfWLj6GIK11rEqecmxqJw0lF5H3o0ToM+9sy3xCErp0Wcm\nob91BfR59+2v1rEqeTdOoHKzUblZ5P11Kn8c7m5D4lEsytfKf3NSoO2XyU08jGXlNn8PQuHjV1j7\nnlQlnaHOnj2b1atXs3LlSoKCgvjvf/+Ln58fW7ZsAWDr1q34+/vj4+NDdHQ0qamppKenExUVRbNm\nzWjdujWbN28GYNeuXfj6+mJlZYWHhwcHDx40OsbD+J8KqPv372fMmDFGZXPnzmXp0qWPqUX3Z2dn\nR3a28Yt3RkYG9g4Od9WxN7x7ui0zIwN7ewc6BgTy8vBR9Hq+M726PUtz31aGlWtOTk707B2Mg6Mj\nLhUq0PelgUTs3I6TU/72AYOHotVqadCwEa3btOX333bh7OLC9JlfUM29OlqtlhGjXycxMZ6zsacN\n554y6T1+Xf8zq9ZuMso809LSeCmoOxVcXflszoL8ttvbFWx7Zib29vb37budnb1RUMu8a0zs7I3H\nIzMzfyxuiz19ih5dOzHmzXfoHdzf6Ph/7I/E3t6e2nXqGcrGjnuPp5+uRNuWTXjr9ZF0DAikbNly\nmBWtFUoZv2lSel1+VvY3jWUZrKoHknt1HzmnloOFNWgt/v6/8f5Kn2u8r0aLTd0B2NQdSF7yaXKT\njt05tX1FbOsNwtqjGzkXt6LPTCLvxnG0Ns5oHSoV2mT9zTNYlK9ZoFzp89CnnMeivJehzMLJHcsK\nDck5u46c2HVo7Ssa7staVfbPXzgVE05u/CG0TtXAwvq+/bVwqYvWsSo5Z34i5/wmtI5Vje7z3mlf\nwXvvecnn0F3aibVHNzuAebMAABcFSURBVMP07/3Gr7D2CdMZPXo0a9eupX///iQnJ9OjRw9sbW0Z\nO3YsQ4cOZciQIYwcORJHR0e6du2KXq+nX79+/Pjjj4wdOxaA0NBQZs2aRd++falWrRp+fn4P1Rb5\nHGoxeNWqzdo1qwzPU1NSSEm+iYfnnReKmt7eXDh/zvBcKcX5c2fxrp1/X2X0G28x+o23AFgRvoQ6\ndesDULWqO7dS70wNaS0ssLCwwMHR8f/bu/e4qOr8j+OvmWEGUEABRUtARMhsNRUpvOQNyxVtW1ul\n7OLvV9l6yWott9a1Mm3NpJur5E8tWy+oSPmzcsvM2p8pLki5ssqW1ywvKCoqIooyzJzfH5OTrKKu\nHEOd99MHj4ecc+bM9wyXN5/z/Zw51A8NpfSMdbYf15UcOcLRoyVVTjG7XC78/Dw/3K9OfImv1ubw\n0bK/ERT807xiZWUlD90/gBtv+gUTJr3hXR4bG8fhQ8WUlZUR9GMg7vhuG/cPeojKyspqjz3+hhbe\n066ex2ynRQvP8Z5e1zzO8xrt2L6dG358LfbtLeTe39zJ2D+9wq/vHnDW671i+TJ69kqpsqxu3bpM\n+Z93vJ8/OfxR2tzW9azHXsssAfUxSrZ5Pzdcp8B1Cot/1T8sbCFNvactjYpSXAc3YLE5sASEVjk9\nbJwq8Z66dR3egjUkxjM/6BeIrX487tJdGPXjcJfuxBbWAgBrYAOsdRrhPlaI+9gu3OUHcf3rx/nv\nypNUbH0fe8wvsQVHevZb+gOOiLZnHYu7rBBLQCgWv8Aqy/0aJeDXKOHHMW32VskWex0czX76nqjY\n/qF3XXXHC2Bv0hmadPYMr+hrLIE/9SC4Tx7BcDuxBDaoMgbXsd04C7NxNP/VT2EK5339zje+a9HP\nOcPyxBNPeP8/e/bss9b37t2b3r17V1l2+trTfxcXF8fChQtrPKYrqkI9n/79+zNq1Cj69+/vvQC3\ntt3WtTu7d+1ibc4awHOpyB29+1ap/FrceBMNGjTwznMuWjCPyOhomsffwN+zV9Gvz+1UVFRQduwY\nM6ZNYeADng7gX/dPJWPOXyg9epTy8nIWZy2ka/dkAPr9JpXp6ZMxDIOdP3zP39esovNt3chfv47f\n3NmL4h/nCDJmzyIyMpqYZrFsyP8H72XOZ8F7H1YJU4B3pqcTFBRcJUwBgkNC6Nbjdm+H7prVX3Jg\n/3463db1vMf+698MYMniLA4c2E9lZSVvT0/n7tR7Pcd1dyqzZk7D5XJRVLSPD/73Pfr1TwXgmZGP\nM+SxJ84ZpuCZV76hxY1Vlk198zXG/vEZALZs/pbVX/4fKX3vuuiv4bXAGhSJUXEMd9lewHMJhzUk\nBovtpyrTcFVwatMCjIpjGIZBZZGn4QbAVj8O15FtGM4TGIYb18GN3urRdXgTlQc3ePZhuHAf24Ul\nMBwsVpyF2Z5Tt3guD3Gf2I8lMBxH818R0OoR7wf2IBw3pHrD1HCegMryswIfwCgvxhoQWmWZ61gh\nFds/xHC7MFwVVB7cgDXUM3bnntVUHvindzvDeRxL3evOe7yuw1uo+OEzDMPAcB73BPQZ1ejpMZw5\n/264nZ7KtFlKlTC90OtX3fiuVZYaflztrpoKdcuWLbz11ls0btyYAQMGsHnzZm688cYLP/AyCgwM\n5J05Cxg96kmOnzhBs9jmpM94l317C7mnX1+yv/L8IM34SwZPPz6MtIkv0bBhBDNmzQOgQ6fbaB4X\nT1LbllitVoaOeNLbyXt3/3vYsulbuiS1JSAgkJS+v+K+B/8bgBf/NIknhj9Ku5uaU7duEK+89mfi\nbmhB3A0tePjRYfS9oxtWq5Xrrr+e2fOzsNlszJs9i6NHS/hlj87e8UdGR/P+h8uY+5dZnDhxnI4J\nrbzrBt6byu/+8CKvT5nG40MfYeH8OQQHB/PuvEz8/T2nx8517ABtExJ57Mmn+FWvHhgYdO/Rk4cf\nHQbAkMeeYNvWzXRI+AV+Nj9+P/o5WrVuQ9G+vXz26cds27qFObPe9o5j3IRJ/LLPnQDsK9xDRKPG\nVb4GAx/8L4Y89ACJrW8gICCQaW/Ppl59zy/q1H592LNrF4V7dvHd9m28+eorPD9uAn3v6mfSd8CV\nwWL1w970lzj3rPbM5/nXxx6djFFRRsWOv+J/431YbA5sDdtQsf0DDMPAFhyFrZGnSc1aJwK/iLZU\nbF/imdcMjsLWwPO9YI9Oxrl7Fac2LQDDjaXudfhFJGCx2bHH9KZyby6V7grAwK/Bzd7QPB/DeRz8\nAs/ZMGY4y8CvTpVl1qDrcJfU49Sm+Z4u2oZtsAV75l5tDVrj3Pk5ruICsPljj+mNxWL1nNqt7njr\nxeI6uoOKTRmAFb/rOmA9I9w946s6BvfR7+HHS3HO5Ii7+7yvX7Xju0ZZfaAJ8HwsRk16hE2Wl5fH\nggULmDp1qndZeno6YWFhZGZm8vHHHwPw0ksvkZiYSJ8+fardV6XLwM/m219cEZGf09rtJRfe6Dw6\nxF3d/Q9XVIUaFhZGaWlplWWHDx8mKCgIt/untnnDMC54OURJ+bXbSfdzaBDkR3FZ5YU3lGpFdRlZ\n20O46pXnv0Vgu8cvvKFUqzz/rdoegs+4os49xMTEUFRUxM6dOwFPmObl5ZGQkMCuXbs4cOAAbreb\nDRs2EBcXd4G9iYjIz8rHJ1GvqArVbrfz+uuv88ILL3gaBgyD559/nvDwcJo1a8bkyZPZvn07CQkJ\nxMef3XIvIiK1R7dvu8K0atWKefPmVVm2Z88e7Hb7OdudRUTkyuDjPUlXXqCKiMjVycfz9MqaQ61O\nZGQkS5Ysqe1hiIiIVEsVqoiImMPHS1QFqoiImEJNSSIiIibw9aakq2IOVURE5EqnClVEREzh4wWq\nAlVEREzi44mqQBUREVOoKUlERMQEakoSERGRGlOFKiIipvDxAlWBKiIiJvHxRFWgioiIKdSUJCIi\nYgI1JYmIiEiNqUIVERFT+HiBqkAVERGT+HiiKlBFRMQUakoSERExgZqSREREpMZUoYqIiCl8vEBV\noIqIiEl8PFEVqCIiYgo1JYmIiJhATUkiIiJSY6pQRUTEFD5eoCpQRUTEJD6eqApUERExha83JWkO\nVURExASqUEVExBS+3uWrQBUREVP4eJ4qUEVExCQ+nqgKVBERMYWakkRERKTGVKGKiIgp1JQkIiJi\ngp8jT1999VX+8Y9/UFlZydChQ2ndujXPPvssLpeLhg0b8tprr+FwOFi6dClz587FarVyzz33kJqa\nitPpZPTo0ezduxebzcYrr7xCVFQUmzdvZty4cQC0aNGC8ePHX9LYdMpXRETMYanhxwWsXbuWbdu2\nkZWVxaxZs5g4cSJTp07l/vvvZ+HChTRt2pTFixdz4sQJpk2bxpw5c8jIyGDu3LmUlJTw8ccfExIS\nQmZmJsOGDeONN94A4OWXX2bMmDEsWrSIsrIyVq1adUmHr0AVERFTWGr470JuueUWpkyZAkBISAjl\n5eXk5eXRs2dPAHr06EFubi4bNmygdevWBAcHExAQQEJCAuvXryc3N5c77rgDgE6dOrF+/XoqKioo\nLCzk5ptvrrKPS6FAFRGRq4LNZqNOnToALF68mK5du1JeXo7D4QAgPDycgwcPUlxcTFhYmPdxYWFh\nZy23Wq1YLBaKi4sJCQnxbnt6H5dCgSoiIqawWGr2cbG++OILFi9ezNixY6ssNwzjnNv/J8ur2/Zi\nKFBFRMQUl3kKFYDs7GxmzJjBO++8Q3BwMHXq1OHkyZMA7N+/n4iICCIiIiguLvY+5sCBA97lp6tP\np9OJYRg0bNiQkpIS77an93EpFKgiImKKy12hHjt2jFdffZWZM2dSv359wDMX+tlnnwGwYsUKunTp\nQps2bSgoKKC0tJTjx4+zfv16EhMT6dy5M8uXLwdg5cqVJCUlYbfbiY2NZd26dVX2cSl02YyIiJjk\n8l44s2zZMo4cOcLIkSO9yyZNmsTzzz9PVlYW119/Pf369cNutzNq1CgGDx6MxWJhxIgRBAcH06dP\nH3JycrjvvvtwOBxMmjQJgDFjxjB27Fjcbjdt2rShU6dOlzQ+i1GTE8ZXsOKyytoewlWtQZCfXsMa\niuoy8sIbyXmV579FYLvHa3sYV7Xy/Ld+tufac6SiRo+PDHWYNJLaoQpVRERMoXdKEhERMYGP56kC\nVUREzKEKVURExAS6fZuIiIjUmCpUERExh28XqApUERExh4/nqQJVRETMoaYkERERE6gpSURERGpM\nFaqIiJjDtwtUBaqIiJjDx/NUgSoiIubw9aYkzaGKiIiYQBWqiIiYwte7fBWoIiJiCp3yFRERkRpT\nhSoiIqZQhSoiIiI1pgpVRERMoaYkERERE/j6KV8FqoiImMLH81SBKiIiJvHxRFVTkoiIiAlUoYqI\niCnUlCQiImICNSWJiIiYwMfzVIEqIiIm8fFEVVOSiIiICVShioiIKdSUJCIiYgJfb0qyGIZh1PYg\nRERErnaaQxURETGBAlVERMQEClQRERETKFBFRERMoEAVERExgQJVRETEBApUEREREyhQRURETKBA\nlQvSe39IbdD3nVxt9NaDcl6GYWD58f3Etm3bhtVqpXnz5metEzGbxWLh66+/ZtWqVXTp0oWbbrqJ\n4ODg2h6WSLX01oNyUTIzM8nKyiIwMJDk5GR++9vfAgpVMdfRo0c5cuQIMTExbNy4kTfeeIN27dpR\nUFBAjx496N69O5GRkbU9TJFzUoUq53RmUK5bt468vDwWLVrEgQMHeOyxx7BYLDz66KMKUzFNRUUF\nGRkZuFwu2rdvz5///Gcef/xxunfvzldffcWKFStYt24dkZGR+kNOrkiaQ5VzOv3LKjc3l23btrF7\n9272799PdHQ0U6ZM4a9//Svp6em1PEq5ljgcDjp06IDb7WbXrl1UVlby7rvvAnDrrbfSs2dPFi1a\nxJEjRxSmckVSoEoVbrcbgMrKSsrKypg4cSIlJSXccccdLF68mB07dtC8eXNef/111qxZw5EjR9Q8\nIjVy8uRJ7/8TExPp2rUrhw4d4oEHHsDhcPD0008DEBsbC8CJEydqZZwiF6I5VDmn4uJiGjRoQG5u\nLt988w1xcXF88803lJeX069fP+Li4nA6ndjt9toeqlzFysrKmDBhAikpKXTr1s27fN26deTk5BAS\nEkJ2djbff/89SUlJpKamkpCQUIsjFqmeKlQBql6ikJ+fT48ePVi5ciVutxvDMIiJieGuu+7C5XKx\nbNkynE4nfn6agpeasdvttG3blg8++IDc3Fzv8sTERG699VY+//xznn32WXr16sXRo0e9Yao6QK5E\ntnHjxo2r7UFI7Ts9J+VyubjuuuvYunUr4PnF9dlnn/Hdd9/Rt29fYmNjSUxMJCgoSPNYUmN+fn7E\nx8fjdDpZunQpoaGhREVFYRgGUVFRbNmyhfj4eHr16sXKlStZvXo1t99+u7735IqkCtXHnfmXfkFB\nAb/73e9wOp0kJydTVFREcnIySUlJrFq1ivnz5xMVFUVYWFgtjliuNf7+/vTp04fk5GQWLlzI6tWr\nsVgsFBQUkJ+fT0BAAEFBQUycOJGnnnqqtocrUi1VqD7szEsPdu/eTWBgIPv27eOjjz6iZ8+e5OTk\nYLFYGDRoEFarlZSUFF1YL5eFn58fsbGx+Pv7k5aWRlFREZmZmYwcOZK2bdsCni7gunXr1vJIRaqn\npiRh3rx55OTkUFZWxrBhw9iwYQONGjXi22+/ZevWrUyZMoXw8PDaHqb4iF27dlFeXo7NZiMuLq62\nhyNy0dRV4oMMw8DtdmOz2di0aRN/+9vfmDNnDtnZ2eTm5hIfH89tt91GkyZNWLduHS6Xq7aHLD4k\nOjq6tocgckkUqD4mJyeH1atX88MPP9ClSxe+//57IiMjsVgsdO3aFbvdzuTJk2nZsiUdO3Zk8eLF\nOByO2h62iMgVT01JPiQvL4/p06fTrVs37rzzToKCgsjPz+fLL7/kgw8+AKBjx460bduWnTt3AujS\nGBGRi6Tflj4iNzeXMWPGMG/ePKKiorzLGzduTFpaGhkZGXz99dd07NiRr776ioceeggAq1V/c4mI\nXAwFqo+oqKjAYrFQWFjovc4PoG3bttx+++3YbDb8/f3ZuXMnb775Jtdff30tj1hE5OqiLl8fkp2d\nzZQpUxg8eDApKSney2aysrLYsGEDEydOxOVyYbPZanuoIiJXHVWoPqRLly643W7vXWJSUlIAz7sk\n1atXD0BhKiJyiRSoPub0G5Cnp6cTGhqK1Wrlww8/ZPz48bU8MhGRq5sC1Qd169YNq9XKqFGjCA4O\nZvr06TRr1qy2hyUiclXTHKoPy8nJoXHjxt77TIqIyKVToIqIiJhAFxmKiIiYQIEqIiJiAgWqiIiI\nCRSoIiIiJlCgyjVjz549tGrVikGDBjFo0CAGDhzIqFGjKC0tveR9vv/++4wePRqAp556iv3791e7\n7fr169m9e/dF77uyspIWLVqctTw9PZ3Jkyef97HJycneGxhcjNGjR/P+++9f9PYi8p9ToMo1JSws\njIyMDDIyMli0aBERERFMnz7dlH1PnjyZRo0aVbt+yZIl/1Ggisi1RW/sINe0W265haysLMBT1aWk\npLB7926mTp3KsmXLmD9/PoZhEBYWxoQJEwgNDWXBggVkZmbSuHFjIiIivPtKTk5m9uzZREVFMWHC\nBP71r38B8PDDD+Pn58fy5cvZuHEjf/zjH2natCnjx4+nvLycEydO8PTTT9OpUyd27NjBM888Q2Bg\nIElJSRcc/8KFC/noo4+w2+34+/szefJkQkJCAE/1XFBQwKFDh3jhhRdISkpi796953xeEbn8FKhy\nzXK5XHz++ee0b9/euywmJoZnnnmGffv2MWPGDO8N1OfOncvMmTMZMWIEU6dOZfny5YSGhjJ8+HDv\n+xyftnTpUoqLi3nvvfcoLS3l97//PdOnT6dly5YMHz6cjh07MmTIEB555BE6dOjAwYMHuffee1mx\nYgXTpk2jf//+3H///axYseKCx3Dq1CneffddgoKCGDt2LEuXLuXBBx8EoH79+sydO5fc3FzS0tJY\nsmQJ48aNO+fzisjlp0CVa8rhw4cZNGgQAG63m8TERO+9XQHatWsHQH5+PgcPHmTw4MGA5/Z2kZGR\n7Ny5kyZNmhAaGgpAUlISmzdvrvIcGzdu9FaXISEhvP3222eNIy8vj+PHjzNt2jTAc6P2Q4cOsXXr\nVoYMGQJAhw4dLng89evXZ8iQIVitVgoLC2nYsKF3XefOnb3HtH379vM+r4hcfgpUuaacnkOtjt1u\nB8DhcHDzzTczc+bMKusLCgqwWCzez91u91n7sFgs51x+JofDQXp6OmFhYVWWG4bhvWm7y+U67z6K\niopIS0vjk08+ITw8nLS0tLPG8e/7rO55ReTyU1OS+KTWrVuzceNGDh48CMCnn37KF198QXR0NHv2\n7KG0tBTDMMjNzT3rse3atSM7OxuAsrIyUlNTvTdwdzqdALRv355PP/0U8FTNL7/8MgDNmzfnn//8\nJ8A5932mQ4cOERoaSnh4OCUlJaxZs4aKigrv+rVr1wKe7uL4+PjzPq+IXH6qUMUnNWrUiOeee46h\nQ4cSGBhIQEAAaWlp1KtXj2HDhvHAAw/QpEkTmjRpwsmTJ6s8NiUlhfXr1zNw4EBcLhcPP/wwDoeD\nzp078+KLLzJmzBiee+45xo4dyyeffEJFRQXDhw8HYMSIEfzhD39g+fLltGvXDj+/6n8EW7ZsSdOm\nTRkwYADR0dE8+eSTjBs3znsLvpKSEoYOHcrevXt58cUXAap9XhG5/PTm+CIiIibQKV8RERETKFBF\nRERMoEAVERExgQJVRETEBApUEREREyhQRURETKBAFRERMYECVURExAT/D5JLr+tPqqRhAAAAAElF\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1bf839e518>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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puz6uZh4d+kf+tutfpJZmcFXMNA4UHOJkRS5fHPua38SORaHxyWPtSUK4hZZs\n+wt21U43365cEtSv0efY7Db+s38FACPCh/Da3rcprC7C4hHE1G4TtKPGAwWHtXFNgJd2/kP7+8zW\nBjiOaK/vMxN3k4dTCNePPR4oOEyUOVJbX/9lGBwykOv7XMeevCT25zu6TH1dzUzqOpYau5WvUzcw\nP/5ONqR/rx2ph3uFclXMdP6x500AYny7MTQ0HlVV+T5zK4XVRXT16YKb0Y19+QdJKU4lyicSu93W\noDs0rzKflYfXkH7a2M3pXa8eJnfty3V596mklWbg6+ZDYu+rqaytYsGmJyi1ljkdFNRPqHI3uZPY\n62othHv790BRFLp4n5pUMavXDP67fwWeLp4MsvTXxtwmRY0lrTSDEM9groieyu68JC2Eh4cOJtw7\nFFVV6eYTSZe6SRohnpYG/5cz9QjoxtGC4w3WT4i8VPt7RNgQRwjjmHRUP0kk4bSJYgbFwNjTJgaB\nI5Tqf+QrrJWNvr/FI5jSmjKGhMRzU2yi02P1Y1xOM3lRsdprifbtRqC7P8dK0lg4+F48XTwos5Zz\nuDBZC0V3kxtUOw6QJnYdQ4JlAOHeofx917+dek8AxnYZycaMhhftCfYIZEzECFRV5aPkz50eMygG\nR/jH/obvkrfQP6gf+wsOsStnD76uPvT27wHAXQNu4evUDVzd4zI8XTwwKAZtxjBAv8DeJIQM5IPD\nH1NmLScuqC95lQV8nbaBuKA+GBQDfQN7EeUTSUZZJt19owB4csRCAG3SHTj+538YOA9/dz/sqp0A\nD38ivMOorK3CtW4il8UzqMFEw9PH6yO9I1AUhcldx+FucufzY19hV+24GExOAdwUV6Mrl3WfrC2n\nl55gU+aPXN59qtPzvF28tL9HhA1mTsLlVBQ13U19OneTO0+PfARwjBenlqTTN6Anl0YMd3re0NB4\n7v7OMQcgyCOQnv4x2pyAXn4xfJLypfY9am7SpqIo3HbJXEpryrTevAHBsU6z7+fFzWVv3n76BPTk\nP/tXEO4VqnXP3xp7PVW2aqLr/n9juozgp+yfmd376gYzthVFaXICoYvRhXsH3U5hVREhXhb83f3I\nqcjDoBgI8PCjsKJ9z+ZpjIRwC6jqqW6XqtpqCqoKMbt4k1GWxfGSNMZHjgYg+bQvcErRce1o+rv0\nzU0GNzjC6MzW4IDgOHbn7iM2sA/z4uZqX/qHh8zHxWDima0vAvCb7pP54tjXWiAPCYln+0lHC+Om\nfnNQFIV4ywAivMMJ9AjAZq/FxejCldHTGNdlNL5uZnr4def9ox/yfdp2evv3oLtvFCGeFmIDe2uh\npygKlwT1Y1Pmj3TziXT6Mb+PrUZGAAAgAElEQVQ65jK+Td/U6Jjk6QHs62rm6h6XYfEMori6hHDv\nMN7Y9w4GxcClEcPxdj31Q+JhcifUK4T8ynzuG3QHJTUl/JT1M4PqTmUBxxfo9wNuxagYtCNcPzdf\nxkSMINIcQULIAOKC+qKqdkpryjlceJTE3tfgYjBxR/8bte3U/4h7u3hpM3QVRXFqmQS6+2stjMaM\n7TKSEd0H8vzmV53W3zNwHn0Dep2qAzcz9w26g4+Ofsa4yFHaJJn6VnBLRPk4DrhGhA1xWj+37yw+\nPPIxV8ZMbexlgKNVNMjSn105e1g45F5qbFYizRG4GV2dTsPydvFiUPAlXNfzKrr6dOHdAx8Cjh+7\nK6JPbf+SoH4NQriXX0yTIWxQDKBAH/+erE//nolntNBmxV3B+JBxdduJZlfOHgaHDtTKFeplYW6/\nWU6vOX02a3yw4/PxyND5FFY5Tl26InoqAy1xThPxPF086FUX7Kc7/WDL380Xf3c/wHGQUP9/PPN0\nqzM5Ppe34ONq1j6XM3r8BoCCqgJ+OLHNaSJfa0Saw3l+9BNOp7DVm9nzSg4WHCbYMwhvVy8qldaP\nZ/5h4DzyKwsJ8mh88ueo8GH8cGIrPc84NdDb1YsocyQ/sK3F71X/OW7KIItj/Lh+2lLfgF68d3AV\n2eUnCfcOdeqRjPKJ5K9jl2jDX63hanQlxMvxf/d3c4Swn5tvq8+J/7VkYlYTamxWSmpK+CZtE25G\nV62bZlT4ULZm76SnX7TWmq0/X3TloTVaV9HgkIHszdtPdTMTHh4f9gAWz2C+S9/MR0dPtQ7+MPA2\nrHYrYV4hjX4hduXsJbv8JNO6TeS57X8lsyyLSO9w7h44j//uX8m0bhOJ8evW4v1181FYs/sbJnYd\nowX+mUpryvj55C+M7TISg2LgSGEKwZ6B+Ln5Ou37mSK8w5gXdz12VSXU69SPnKqq7MzZTTefrk6n\nFNU7WZFLrb3Wqcv1XFBVldf2/pdIcxemd5941ufWn/sa5hVCF+9wzK7eDA4ZSJRPJBUuxTy4znFt\n9d90m8SmzC08NeLhJs9DBMdBnaIo2iSplqqxWTEZjK063/XUe1ZRVF3i9L9ozpHCFHbl7uXqmN9o\np/MA5Fbk8+RPL2gz6MExTn2o4Airjn7G6PBh2oz1v41b6vQjmVmWRZhXiNM+nP59rrHV8F3694yJ\nGI5nI6FzuvoW2vOjnzjrOdAt8c6BD9mStZ0pUeO5KmZ6m7Z1Jqu9lrSSjFZ9N3+Nc/W7WGuvJaPs\nRIMzC8BxatkzW5cxImwI1/e9rt3fGxy9jdB8S/vX+sfu/2Nf/kHCvEL42+VPnpeJWRLCdT5LWcf6\n9B8YaIljbt9Z2hexJUI9LcQF9eW79M24G92oqD3VXehp8mCQ5RL25O53Gtust2zMM7ib3Pgldx+v\n1U2Rv2fAPPoG9mrw3KYcLTrGN2kbmN3rau3IvbXaWo9fHV/PxymOWdULEu7mZEUuVbZqPjz8Mdf2\nvMKpS1bP7KqdjLITWjfj6YKCvHlz2yr6BvQ66/mznU16aSaB7gHsyt3Dntz93H7JDRgNRoqrS6m2\nVfPUT45T0s48Na0xv/ZzmF6aSUlNKbGBfVr92jPZ7DZ25yXRx79Hs+F/oeqos0aKqovxdvH6VS3S\nC8Hfdv6Lw0XJRJkj+fNvFl0YF+vo7Kz2Wj5P+UrrZvwp62eOFh0j7yyzXv3d/Kix1WgzPbMrcshO\nc8xOnNptAhaPIO2qN6MjhnNVzHSu61XLntwk0koztFY1oLWSevlF4+tqZmLXsa0KYIAeft07/Ef/\n9K7k7r5R2lhbjG93wr0aTkzRqzPHIE+nKIpTV+3FItLsuEreqPBhjAofpq33dTOjqo4zAerH7s51\nGdqD0WDUruAlWqepK5fpRf/gWA4XJTM4ZMB5e8+LNoQ3ZWwhuyKHHn7dtQA2u3hTai07awCDYxws\nNrA3Kw59pM0YDfMKYUzESEZHDMOgGHh82AI+Tv6SEXUzEF0MJhJCBpAQMoBR4UN56qc/O23T08WT\npaMfb/8dPU+aOvI931efERcWRVF4eMh9HV0MIVpkXJdRdPPpSrdmxqvb00UZwgcLjrDy8EcA2rll\nv+tzHSU1JdppOGYXb+bH38mOnN1OJ3sD9PSPJi6oL88E9uGe9Y5ZlQsS7na6fmuol8Vp8s/pAt0D\nMClGp0lGetc3oBcmxciV7TyGJoQQ54uiKHT3bTjefS5dVCFsV+38cGKr02XN6idX9QvspV1aEByn\nLLib3Ijwajhrtb7r1zHN/gZqbdYGF1A/G6PByF/HLW313WUuZGZXb/42vvErLQkhhGjcRRXCW7K2\ns+KQowVsUox4u3pTVF2Mu9EdX1cf7a4ncGqsNi6oLyPDhjI8bDCuRhfyKwucTlEYGBz3q8rSmQJY\nCCHEr3NRhLDVXst7B//ndP3gMO9Q4gL78uXxbwjzsqAoCiGewVg8gpzO6TUZTPyu70xtuT0ngAgh\nhLi4dfoQLqkp5fW9bztdSAMcFyMYETaEb9M3EV13zp7JYGpwVxghhBDiXOnUIVxjq+GVX14ns+6m\n4XN6X8tb+98nKf+g424cHv48PeJhPFoxniuEEEK0l9ZfbkdH9uUfJLMsi2GhCdwS+zs8XTz4bZ9r\nGRwykKt7XAY4JhTp9cRyIYQQ+tap0yel6DgAI8OHOl1b+ObY33ZgqYQQQgiHTtcSVlVVu51bcvFx\njIqxySscCSGEEB2p04Xwl8e/4eHvn2Zv3n4yyk7Q1RzR5A0JhBBCiI7Uqbqj7aqdz+uublV/Y/fR\nZ9wTUwghhLhQdKqW8NG6e7PW6+Pfk2GhCR1UGiGEEOLsOlUIZ5RlOS2PixwlV6YSQghxwepU3dE5\nFXkATIuaQHltZbvcW1QIIYQ4VzpVCOfWhfDkqPHatZ+FEEKIC1Wn6o4+WZGLn5uvBLAQQghd6DQh\nXGOzUlRdjMUjqKOLIoQQQrRIpwnhzLIsVFRCvSwdXRQhhBCiRTpNCB8pTAagh190B5dECCGEaJlO\nE8KHCo8C0Ms/poNLIoQQQrRMpwhhVVU5VpJKqKcFs6t3RxdHCCGEaJFOEcJl1nKqbTWEeAZ3dFGE\nEEKIFusUIZxfVQBAoEdAB5dECCGEaLnOEcKVhQAEuPt3cEmEEEKIlmvRFbOWLl3K7t27URSFRYsW\n0b9/f+2xd999l08++QSDwUBcXByPPvroOStsUwqqHCEcJC1hIYQQOtJsS3jbtm2kpqaycuVKlixZ\nwpIlS7THysrKeOONN3j33Xd5//33SU5O5pdffjmnBW5MXl13tLSEhRBC6EmzIbxlyxYmTZoEQExM\nDMXFxZSVlQHg4uKCi4sLFRUV1NbWUllZia+v77ktcSMK6rqjAyWEhRBC6EizIZyXl4e//6lwCwgI\nIDc3FwA3NzfuvvtuJk2axPjx4xkwYADdu3c/d6VtQpm1DFeDC+4m9/P+3kIIIcSv1eq7KKmqqv1d\nVlbGv/71L9auXYu3tzc33ngjBw8epE+fpm8h6O/viclk/HWlbUK1WoOXmyfBweZ23e7FRuqv7aQO\n207qsH1IPbbd+ajDZkPYYrGQl5enLefk5BAc7DgfNzk5mcjISAICHBOiBg8ezL59+84awoWFFW0t\ns5PgYDPl1RV4u3iRm1vartu+mAQHm6X+2kjqsO2kDtuH1GPbtXcdNhXozXZHjxo1inXr1gGQlJSE\nxWLB29txVaqIiAiSk5OpqqoCYN++fXTr1q2ditwyqqpSUVuJh8njvL6vEEII0VbNtoTj4+OJjY0l\nMTERRVFYvHgxq1evxmw2M3nyZG699VZuuOEGjEYjgwYNYvDgweej3JpqWw121Y6HjAcLIYTQmRaN\nCS9YsMBp+fTu5sTERBITE9u3VK1QYa0EkBAWQgihO7q/YlZFjYSwEEIIfdJ/CGstYRkTFkIIoS+6\nD+Fyq2O2taeEsBBCCJ3RfwjXdUfLhTqEEELoje5DuL472lNCWAghhM50mhCWlrAQQgi96TQh7Oki\nY8JCCCH0pdOEsJvRrYNLIoQQQrSO7kO4xmYFwMXg0sElEUIIIVpH9yFsrQthV6OEsBBCCH3RfQhL\nS1gIIYRedaIQbvWtkYUQQogOpfsQru+ONkkICyGE0Bndh3CNzYrJYMKg6H5XhBBCXGR0n1w1Nqt0\nRQshhNAl3Yew1WaVSVlCCCF0SfchXCMhLIQQQqc6QQjXSHe0EEIIXdJ/CNtrcZELdQghhNAhXYew\nqqp1LWEJYSGEEPqj6xC2q3ZUVZXuaCGEELqk6xCuscslK4UQQuiXrkPYWh/CMiYshBBCh/QdwrZa\nQK4bLYQQQp/0HcLSHS2EEELHOkkIS0tYCCGE/ug8hOu7o6UlLIQQQn/0HcI2mZglhBBCv/QdwtId\nLYQQQsd0HsLSHS2EEEK/dB7Cjpawq4SwEEIIHdJ1CHuaPADwc/fr4JIIIYQQrdeiwdSlS5eye/du\nFEVh0aJF9O/fH4CTJ0+yYMEC7Xnp6ek88MADXHHFFeemtGeIC+rLP65Yiq3MeF7eTwghhGhPzYbw\ntm3bSE1NZeXKlSQnJ7No0SJWrlwJQEhICG+//TYAtbW1zJ07lwkTJpzbEp8h0NOf3PLS8/qeQggh\nRHtotjt6y5YtTJo0CYCYmBiKi4spKytr8LyPPvqIqVOn4uXl1f6lFEIIITqhZkM4Ly8Pf39/bTkg\nIIDc3NwGz/vwww+ZOXNm+5ZOCCGE6MRafYKtqqoN1u3atYvo6Gi8vb2bfb2/vycmU/uO4QYHm9t1\nexcrqce2kzpsO6nD9iH12Hbnow6bDWGLxUJeXp62nJOTQ3BwsNNzNmzYwIgRI1r0hoWFFa0s4tkF\nB5vJzZUx4baSemw7qcO2kzpsH1KPbdfeddhUoDfbHT1q1CjWrVsHQFJSEhaLpUGLd+/evfTp06cd\niimEEEJcPJptCcfHxxMbG0tiYiKKorB48WJWr16N2Wxm8uTJAOTm5hIYGHjOCyuEEEJ0Ji0aEz79\nXGCgQav3008/bb8SCSGEEBcJXV8xSwghhNAzCWEhhBCig0gICyGEEB1EQlgIIYToIBLCQgghRAeR\nEBZCCCE6iISwEEII0UEkhIUQQogOIiEshBBCdBAJYSGEEKKDSAgLIYQQHURCWAghhOggEsJCCCFE\nB5EQFkIIITqIhLAQQgjRQSSEhRBCiA4iISyEEEJ0EAlhIYQQooNICAshhBAdREJYCCGE6CC6DuHK\n6lo+/z6Fqpraji6KEEII0Wq6DuFfjubxz4/28svRvI4uihBCCNFqug5hu10FwGq1d3BJhBBCiNbT\ndQgbDAoANlXt4JIIIYQQrafrEDbWhbBqlxAWQgihP7oOYYNS1xKWEBZCCKFD+g7hupawZLAQQgg9\n6hwhLCkshBBCh/Qdwlp3tMyOFkIIoT+6DmGjdEcLIYTQMV2HcF0GS3e0EEIIXdJ3CBtkdrQQQgj9\nMrXkSUuXLmX37t0oisKiRYvo37+/9lhWVhZ//OMfsVqt9OvXj6effvqcFfZMRoPjGEKVi3UIIYTQ\noWZbwtu2bSM1NZWVK1eyZMkSlixZ4vT4888/zy233ML//vc/jEYjJ06cOGeFPZNSV3ppCQshhNCj\nZkN4y5YtTJo0CYCYmBiKi4spKysDwG63s2PHDiZMmADA4sWLCQ8PP4fFdWaUU5SEEELoWLMhnJeX\nh7+/v7YcEBBAbm4uAAUFBXh5efHcc88xZ84cli1bdu5K2oj6U5QkhIUQQuhRi8aET3f6+Kuqqpw8\neZIbbriBiIgIbr/9djZs2MC4ceOafL2/vycmk/FXFfZMFbWOsri6mQgONrfLNi9mUodtJ3XYdlKH\n7UPqse3ORx02G8IWi4W8vFP3683JySE4OBgAf39/wsPD6dq1KwAjRozgyJEjZw3hwsKKNhb5lOJi\nx7YqKmrIzS1tt+1ejIKDzVKHbSR12HZSh+1D6rHt2rsOmwr0ZrujR40axbp16wBISkrCYrHg7e0N\ngMlkIjIykuPHj2uPd+/evZ2K3Dy5gYMQQly4Nmz4ttWv+eWXnRQWFjit27nzZx577KH2KtYFpdmW\ncHx8PLGxsSQmJqIoCosXL2b16tWYzWYmT57MokWLePjhh1FVlV69emmTtM4HuXa0EEJcmLKyTvDN\nN+sYN25iq173+eefMGfO9fj7B5yjkl1YWjQmvGDBAqflPn36aH9HRUXx/vvvt2+pWujUZSslhIUQ\n4kLyl7+8wIEDSbz55mvMnv1bli59itLSUmw2G/PnP0iPHj1555232LhxPQaDgVGjLqVv335s3ryB\nY8dSePbZPxEaGtpgu99++zUrV76L0Wikd+++zJ+/gMOHD7Js2Qu4uLjg6urKU089R1ZWZoN1ZvOF\nN07e6olZFxJFuqOFEKJZH3x3lO0Hc9p1m0P6WJg1oUeTj8+ZM5fVqz/g5ptv4623XmfYsJFcccUM\njh1L4W9/e5G//vVVVqx4hzVr1mI0GlmzZhVDhgynR49e/PGPDzUawBUVFfz738t588338PT05KGH\n7mfnzp/ZtGk9V189k2nTLmPHju0UFOTzxRefNlgnIdzO5AYOQghx4du7dw9FRYWsW/cFANXVVQCM\nGzeR+fN/z+TJ05gyZVqz20lPT6NLl654enoCMGhQAocPH2T06LG8+OLzpKenMXHiZKKiujW67kKk\n6xCWMWEhhGjerAk9ztpqPddcXEzcf/+DxMX1d1q/YMEjpKYe57vvvuYPf7iDf//7P2fdjqI4nyZb\nW2vFzc2NwYOH8vrr/+XHHzfz7LNPcs898xtdFx8/uN33ra30fQMHuViHEEJckAwGAzabDYB+/eLY\ntGkDAMeOpbBixTuUlZXx5puvERXVjZtvvg2z2ZeKinKn150pMjKKjIw0KirKAdi1aye9e/dj1aqV\nlJQUM2XKdGbP/i2HDx9sdN2FSNctYZmYJYQQF6aoqO4cOnSQv/99GfPm3cmSJU/y+9/Pw263M3/+\nAry9vSkqKuS2227Aw8OTuLj++Pj4MnBgPI89tpDnnltGdHSM0zY9PDy4++77eOCBP6AoBvr3H8iA\nAQOprKzg8ccfxtvbGxcXFxYtWszhw4carLsQKep5vgVRe578bK21cceLG4ntHsADswe223YvRnJy\nf9tJHbad1GH7kHpsuwvmYh0XMhkTFkIIoWf6DmEZExZCCKFjug5hRVEwKGCTMWEhhBA6pOsQBscM\nPFVawkIIIXRI9yFsNCpyxSwhhBC6pPsQNiiKjAkLIYTQJd2HsNGgyHnCQghxAWqvWxl2ZroPYYNB\nuqOFEOJCU38rw9b6/PNPLqoQ1vUVs6CuJSwhLIQQF5T2vJVhbW0tS5Y8SW5uDpWVldxyy+2MGnWp\ndgtDg0EhLm4Ad999X6Pr7rnndv74x4eIju7BqlUrKSoqYtCgBFaseIeKigruued+du3awYYN32K3\n2xkxYhQLFz5AaWkpTz/9GOXl5Xh7e/PEE89yyy2/46233sfT05M9e35hxYp3Wbr0z7+6njpHCEt3\ntBBCNGn10c/YlbO3Xbc5yHIJ1/S4vMnH2/NWhqWlJQwdOpzp0y8nMzODxx9/mFGjLuWvf32RBx9c\nRI8ePXnmmSfIzs5qdF1TkpOP8v77q3F1dWXXrh28+urrGAwGZs26irvvvoP333+boUNHcN11iaxc\n+S47d/7MmDHj+f77TUyZMo3vv9/I5MlT21SPug9hg0HBZrN3dDGEEEI0oa23MjSbfThwIIlPPlmN\nohgoKSkGIC0tlR49egLw+ONPN7muKT169MTV1RUAd3d37rnndoxGI0VFRRQVFXH48EHmzbsLgNmz\nfwdAeHgEr7/+D6ZMmcauXTu49dY7f1Wd1NN9CBsNBqy1EsJCCNGUa3pcftZW67nW1lsZfv31WkpK\nSli+/HVKSkqYN28u4LhOxJkaW6fUXV0RHF3bp8rlAkB2dhYrV77L//3fu3h6ejJ37qy6bRlRVed8\n6dGjJ/n5+Rw4kET37jG4ubm1pAqaJBOzhBBCtLv2vJVhUVERYWHhGAwGNm78DqvVCkC3bt1JStoH\nwHPPPc3x48caXefl5UV+fh4Ae/fublDWoqIi/P398fT05NChg2RnZ2O1Wunbtx87dmwHYM2aVXz5\n5WcATJgwmb/85QUmT2669d7iemrzFjqYQSZmCSHEBef0WxnOnDmbzMx0fv/7ebzwwrMMHBjvdCvD\ne++9k9jYOKdbGaakJGvbGjduAj/+uJn77rsLDw8PLBYLb775Gvfdt4BXXnmJu+66FbPZh27duje6\n7sorr2HZsj/x4IP3ERQU3KCsPXv2wsPDk7vuuoVvv/2Kq666hqeeeorrrpvDvn17uOee2/nxx+8Z\nO3Y8ABMnTiYnJ4eEhCFtridd38oQ4On//ExOQQWv3D+mXbd7sZFbn7Wd1GHbSR22D6nHtjtbHX7+\n+SdkZ2dx6613tGp7jdH9mLDBoMgNHIQQQpwXL7zwLCdOZPLccy+2y/Z0H8JynrAQQojzZeHCx9p1\ne7ofE5YQFkIIoVe6D2GZmCWEEEKvdB/CRoMBFeSqWUIIIXSnE4Sw4yRsaQ0LIYTQG92HsEFCWAgh\nhE51nhCW7mghhBA6o/sQlu5oIYQQeqX7EK5vCcv1o4UQQuiN7kNYawlLBgshhNAZ3YewTMwSQgih\nVy26bOXSpUvZvXs3iqKwaNEi+vc/dU/ICRMmEBoaitFoBODFF18kJCTk3JS2EUatO1ruKSyEEEJf\nmg3hbdu2kZqaysqVK0lOTmbRokWsXLnS6TmvvfYaXl5e56yQZ2Osu4GzNISFEELoTbPd0Vu2bGHS\npEkAxMTEUFxcTFlZ2TkvWEtJd7QQQgi9arYlnJeXR2xsrLYcEBBAbm4u3t7e2rrFixeTmZlJQkIC\nDzzwAIqiNLk9f39PTCZjG4t9Sn13tJ+fZ5P3axQtI/XXdlKHbSd12D6kHtvufNRhq29lqJ5xUYx7\n772XSy+9FF9fX+6++27WrVvHtGnTmnx9YWFF60t5FvUhnJtXhrvup5l1HLkJeNtJHbad1GH7kHps\nu/auw6YCvdnYslgs5OXlacs5OTkEBwdryzNmzCAwMBCTycSYMWM4fPhwOxS35Uwmxy7U2mRilhBC\nCH1pNoRHjRrFunXrAEhKSsJisWhd0aWlpdx6663U1NQAsH37dnr27HkOi9uQq4uja9taKyEshBBC\nX5rtjo6Pjyc2NpbExEQURWHx4sWsXr0as9nM5MmTGTNmDLNnz8bNzY1+/fqdtSv6XHCtawlbpSUs\nhBBCZ1o0JrxgwQKn5T59+mh/33jjjdx4443tW6pWcDFJS1gIIYQ+6X4qk6tL3ZiwhLAQQgid0X0I\nu9R3R0sICyGE0JlOEMJ13dEyJiyEEEJndB/C9d3R0hIWQgihN/oPYZmYJYQQQqd0H8KnxoRtHVwS\nIYQQonV0H8LaxTpkTFgIIYTO6D6EZXa0EEIIvdJ9CMtlK4UQQuiV/kNYWsJCCCF0SvchLOcJCyGE\n0Cvdh7CcJyyEEEKvdB/CMjFLCCGEXuk+hGVilhBCCL3SfQgbDQqKImPCQggh9Ef3IawoCi4mg7SE\nhRBC6I7uQxjAxWiQ+wkLIYTQnc4RwtISFkIIoUOdJ4RlTFgIIYTOdJIQNkpLWAghhO50jhA2SktY\nCCGE/nSOEDYZsFrtqKra0UURQgghWqxThLC7mxG7qlJjldawEEII/egUIezt4QJAWaW1g0sihBBC\ntFynCGEvd0cIl1dJCAshhNCPThHC9S3hcmkJCyGE0JFOEcJe7iYAyqpqO7gkQgghRMt1jhCWlrAQ\nQggd6hQhLBOzhBBC6FGnCGGZmCWEEEKPOkUIe3s4xoTLK2VMWAghhH50ihD2ku5oIYQQOtSiEF66\ndCmzZ88mMTGRPXv2NPqcZcuWMXfu3HYtXEt5uJlQFOmOFkIIoS/NhvC2bdtITU1l5cqVLFmyhCVL\nljR4ztGjR9m+ffs5KWBLGBQFH09XisqqO6wMQgghRGs1G8Jbtmxh0qRJAMTExFBcXExZWZnTc55/\n/nnuv//+c1PCFgrydaegpBq7XW7iIIQQQh9MzT0hLy+P2NhYbTkgIIDc3Fy8vb0BWL16NUOHDiUi\nIqJFb+jv74nJZPyVxW1ccLCZiBAzySdKUFxNBPt7tuv2LxbBweaOLoLuSR22ndRh+5B6bLvzUYfN\nhvCZTr9dYFFREatXr+bNN9/k5MmTLXp9YWFFa9/yrIKDzeTmlmKuu2rW4ZQ8lK7+7foeF4P6ehS/\nntRh20kdtg+px7Zr7zpsKtCb7Y62WCzk5eVpyzk5OQQHBwPw008/UVBQwO9+9zvuuecekpKSWLp0\naTsVuXWCfN0ByCuu6pD3F0IIIVqr2RAeNWoU69atAyApKQmLxaJ1RU+bNo0vvviCDz74gFdeeYXY\n2FgWLVp0bkvchCA/DwByiyo75P2FEEKI1mq2Ozo+Pp7Y2FgSExNRFIXFixezevVqzGYzkydPPh9l\nbJH6lnB+ibSEhRBC6EOLxoQXLFjgtNynT58Gz+nSpQtvv/12+5TqV/DzcgOguLymw8oghBBCtEan\nuGIWgJurETdXIyVlEsJCCCH0odOEMICvl6u0hIUQQuhGpwvhkooauWCHEEIIXeh0IayqUCo3chBC\nCKEDnSyEHZOzPv3hGK+s3ovNbu/gEgkhhBBNa/UVsy5kPt6uAHy3MxOAE3kVRFq8O7JIQgghRJM6\nWUvY1Wn5RF55B5VECCGEaF6nCuEAs5vTckZuWRPPFEIIITpepwrh8CAvp+WMHAlhIYQQF65OFcL+\nZ7SE0ySEhRBCXMA6VQgriuK0XFhaTWFpdQeVRgghhDi7ThXCACajI4jNni4ApJwo6cjiCCGEEE3q\ndCH88O8SGNgjiBunOW4ykXKiuINLJIQQQjSuU50nDBAd7sO9M/tTUVULQLqMCwshhLhAdbqWcD1P\ndxNe7ia5v7AQQogLVveVDc8AACAASURBVKcNYYBAH3fyi6tQVbmhgxBCiAtP5w5hX3dqau1yQwch\nhBAXpM4dwj7uAOQXS5e0EEKIC0/nDmFfCWEhhBAXrs4dwvUtYZmcJYQQ4gLUqUO4S91tDI9myrnC\nQgghLjydOoRD/D0I9nNn//ECam32ji6OEEII4aRTh7CiKPSPDqKy2sbBtMKOLo4QQgjhpFOHMMDw\nuBAAvvwpDYCCkipeXrWHAhknFkII0cE63WUrzxQT7ktsN3+SjhdyJKOIz7eksic5H1WFe2f27+ji\nCSGEuIh1+pYwwBWjugPw2qf72ZuSD0BNra0jiySEEEJcHCHcK9KPwb2DySuuov4KlkaDY9cPpRVS\nXF7TgaUTQghxsbooQhjgrhlx+Hq7asuKAhk5Zbzw3i7+/P6uDiyZEEKIi9VFE8KKotAj3FdbLq+y\nknqyFIATeeUdVSwhhBAXsYsmhAFCAz21v0vKazhZWNmBpRFCCHGxu6hC2Ozhov1dUm4lI6cMAFeX\nU9WwfmcGyXKFLSGEEOfBRRXCPSP9tL+rrTbtcpYGRQEcreO3vzrMkrd3dEj5hBBCXFxaFMJLly5l\n9uzZJCYmsmfPHqfHPvjgA2bNmkViYiJPPvkkav304wtQ9zAfnpk3jMF9LACU1d1nuKrGhrXWri0L\nIYQQ50OzIbxt2zZSU1NZuXIlS5YsYcmSJdpjlZWVfP7557z77rusWLGClJQUdv1/e2ceGEV5///3\n7L2bzZ1sSMhJgCRAQriVGwQqolaxKlp+1AtqEXuJyldtbfttPahSLVVBRat4oaCIoIIgNyFAAiEJ\nAXJf5NxNdrP3Nb8/Zmd2JrubA0IW/D6vvzazk9lnnnnm+ZzP5zl9bWcaD40JQWJMiM9xo8URUAiX\nVGuJgCYQCATCgNOrEM7Ly8O8efMAAOnp6dDr9TAamViqUqnEBx98AKlUCovFAqPRiNjY2Kvb4gFg\ngscSBoB4T7KW0eJAl9kraB1OZsOHykY91m0pwtpPCqEzWFHb3DW4jSUQCATCoNBptGHjjtJBrR3R\na9nK9vZ2jB49mvs7KioKbW1tUKvV3LG3334bH374IZYtW4akpKQerxcZqYJEIr6CJvsSGxt62edP\nGROP7QcrIZZJQEm8OolELkVslAplDUzcuKHNhCffOgaaBra+dCvk0oG9h2uB/vYjwRfSh1cO6cOB\ngfRj/9mw4xxOnGsBKArPPRQ9KH3Y79rR/mK+K1aswLJly7B8+XJMmDABEyZMCPj/HR3m/v5kj8TG\nhqKtrf/W6dpHb0SXxYGqSwYAQEOTHq28JUuVdTpQLhc6O73H2Fsvr25HXCRjQV+o64DbTSMrNapf\nv6832iCXiaGQXRvluy+3HwleSB9eOaQPBwbSj5eHvovZ2EenZ+b9gezDQAK9V3e0RqNBe3s793dr\nayvncu7s7MTJkycBAAqFAjNnzkRhYeFAtPeqExOhRFp8GNSeZUsbvi5FDc/V/MLmAtQ2d6HLTyxY\np/fuwPTyJ6fxz8/O9Ou3HU43/vCfo/jr+ycvs/XXJtdyUh6BQCD0hkjErJRxD+Jc1qsQnjZtGnbv\n3g0AKC0thUaj4VzRTqcTa9asgcnEVJwqLi5GWlraVWzuwJOZEgmxp+MLL7YJvttzsh4X/OxDrDXY\nfI6ZrX1P3GLP/SkVCzGY7Xj45f349nhtsJtCIBAIlwUnhN2DJ4R79YWOHz8eo0ePxpIlS0BRFJ5/\n/nl8+eWXCA0Nxfz58/HYY49h2bJlkEgkyMjIwE033TQY7R4wwkNk+N9HpuCZt4/7fJdX2uz3f3Qe\nl4XN7t2JqbXTgtQhUr/nd8dkdV5GS69tymoYZWXrgUrcckNKkFtDIBAI/UfMCeHB+80+BSRXr14t\n+DszM5P7vHjxYixevHhgWzXIxEUqoVZKYbQ4IBGLsGrxGLz2xdmA5+sMVlQ26vHOznPcsa8OVWNG\nTjzGj4zltKlAmH+CQpi4ogmBeGFzAVLjQ3H/vJHBbgqB0CNs4SbXIFrC/6cqZgWCoihkJjPVtKLC\n5MhJj+nxfK3Bhl15tYJEruIqLd7cXoLT5YxLu9Now9s7SqH1xI+7zHZ8uPsCNu++gBc+8l+Ry2p3\nDqobZCAZzBgK4dqkrEaHrw5VCRQym52pTLf3VEMQW0YYDNxuGp/tK7+ul3E6XIwJPJhGxbWRmnsN\nsOzmTEzI0CBJw8S7I0Pl6Ojyjf0CzBaIpgAx4JrmLkzI0GDTrjKUVutQdcmA+ZOScPBMIxrafHdr\nctM0RBSFji4bnnjjKGaOjccDC7MG7sYGicF03xC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mFAjK1g5LwCQvhUyMxFjvXvYWj3LI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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1b71f87630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "J9W2wtvdwuJV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Train XGB Boost"
      ]
    },
    {
      "metadata": {
        "id": "Q6ddo_A9wt9R",
        "colab_type": "code",
        "outputId": "33f42e44-a151-40d7-e596-e1b10eb518c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 26292
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "# from gan import GAN\n",
        "import random\n",
        "import tensorflow as tf\n",
        "import xgboost as xgb\n",
        "from sklearn.externals import joblib\n",
        "from sklearn.metrics import confusion_matrix\n",
        "# from plot_confusion_matrix import plot_confusion_matrix\n",
        "\n",
        "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"\n",
        "\n",
        "class TrainXGBBoost:\n",
        "\n",
        "    def __init__(self, num_historical_days, days=10, pct_change=0):\n",
        "        self.data = []\n",
        "        self.labels = []\n",
        "        self.test_data = []\n",
        "        self.test_labels = []\n",
        "        \n",
        "        assert os.path.exists(f'{googlepath}models/checkpoint')\n",
        "        gan = GAN(num_features=5, num_historical_days=num_historical_days,\n",
        "                        generator_input_size=200, is_train=False)\n",
        "        with tf.Session() as sess:\n",
        "            sess.run(tf.global_variables_initializer())\n",
        "            saver = tf.train.Saver()\n",
        "            if os.path.exists(f'{googlepath}models/checkpoint'):\n",
        "                    \n",
        "                    with open(f'{googlepath}models/checkpoint', 'rb') as f:\n",
        "                        model_name = next(f).split('\"'.encode())[1]\n",
        "                    filename = \"{}models/{}\".format(googlepath, model_name.decode())\n",
        "                    currentStep = filename.split(\"-\")[1]\n",
        "                    new_saver = tf.train.import_meta_graph('{}.meta'.format(filename))\n",
        "                    new_saver.restore(sess, \"{}\".format(filename))\n",
        "            files = [os.path.join(f'{googlepath}stock_data', f) for f in os.listdir(f'{googlepath}/stock_data')]\n",
        "            for file in files:\n",
        "                print(file)\n",
        "                #Read in file -- note that parse_dates will be need later\n",
        "                df = pd.read_csv(file, index_col='timestamp', parse_dates=True)\n",
        "                df = df[['open','high','low','close','volume']]\n",
        "                # #Create new index with missing days\n",
        "                # idx = pd.date_range(df.index[-1], df.index[0])\n",
        "                # #Reindex and fill the missing day with the value from the day before\n",
        "                # df = df.reindex(idx, method='bfill').sort_index(ascending=False)\n",
        "                #Normilize using a of size num_historical_days\n",
        "                labels = df.close.pct_change(days).map(lambda x: int(x > pct_change/100.0))\n",
        "                df = ((df -\n",
        "                df.rolling(num_historical_days).mean().shift(-num_historical_days))\n",
        "                /(df.rolling(num_historical_days).max().shift(-num_historical_days)\n",
        "                -df.rolling(num_historical_days).min().shift(-num_historical_days)))\n",
        "                df['labels'] = labels\n",
        "                #Drop the last 10 day that we don't have data for\n",
        "                df = df.dropna()\n",
        "                #Hold out the last year of trading for testing\n",
        "                test_df = df[:365]\n",
        "                #Padding to keep labels from bleeding\n",
        "                df = df[400:]\n",
        "                #This may not create good samples if num_historical_days is a\n",
        "                #mutliple of 7\n",
        "                data = df[['open','high','low','close','volume']].values\n",
        "                labels = df['labels'].values\n",
        "                for i in range(num_historical_days, len(df), num_historical_days):\n",
        "                    features = sess.run(gan.features, feed_dict={gan.X:[data[i-num_historical_days:i]]})\n",
        "                    self.data.append(features[0])\n",
        "#                     print(features[0])\n",
        "                    self.labels.append(labels[i-1])\n",
        "                data = test_df[['open','high','low','close','volume']].values\n",
        "                labels = test_df['labels'].values\n",
        "                for i in range(num_historical_days, len(test_df), 1):\n",
        "                    features = sess.run(gan.features, feed_dict={gan.X:[data[i-num_historical_days:i]]})\n",
        "                    self.test_data.append(features[0])\n",
        "                    self.test_labels.append(labels[i-1])\n",
        "\n",
        "\n",
        "\n",
        "    def train(self):\n",
        "        params = {}\n",
        "        params['objective'] = 'multi:softprob'\n",
        "        params['eta'] = 0.01\n",
        "        params['num_class'] = 2\n",
        "        params['max_depth'] = 20\n",
        "        params['subsample'] = 0.05\n",
        "        params['colsample_bytree'] = 0.05\n",
        "        params['eval_metric'] = 'mlogloss'\n",
        "        #params['scale_pos_weight'] = 10\n",
        "        #params['silent'] = True\n",
        "        #params['gpu_id'] = 0\n",
        "        #params['max_bin'] = 16\n",
        "        #params['tree_method'] = 'gpu_hist'\n",
        "\n",
        "        train = xgb.DMatrix(self.data, self.labels)\n",
        "        test = xgb.DMatrix(self.test_data, self.test_labels)\n",
        "\n",
        "        watchlist = [(train, 'train'), (test, 'test')]\n",
        "        clf = xgb.train(params, train, 1000, evals=watchlist, early_stopping_rounds=100)\n",
        "        joblib.dump(clf, f'{googlepath}models/clf.pkl')\n",
        "        # cm = confusion_matrix(self.test_labels, map(lambda x: int(x[1] > .5), clf.predict(test)))\n",
        "        cm = confusion_matrix(self.test_labels, list(map(lambda x: int(x[1] > .5), clf.predict(test))))\n",
        "        print(cm)\n",
        "        plot_confusion_matrix(cm, ['Down', 'Up'], normalize=True, title=\"Confusion Matrix\")\n",
        "\n",
        "tf.reset_default_graph()\n",
        "boost_model = TrainXGBBoost(num_historical_days=HISTORICAL_DAYS_AMOUNT, days=DAYS_AHEAD, pct_change=PCT_CHANGE_AMOUNT)\n",
        "boost_model.train()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"Relu:0\", shape=(?, 20, 1, 32), dtype=float32)\n",
            "Tensor(\"Relu_1:0\", shape=(?, 20, 1, 64), dtype=float32)\n",
            "Tensor(\"Relu_2:0\", shape=(?, 18, 1, 128), dtype=float32)\n",
            "2304\n",
            "Tensor(\"Relu_4:0\", shape=(?, 20, 1, 32), dtype=float32)\n",
            "Tensor(\"Relu_5:0\", shape=(?, 20, 1, 64), dtype=float32)\n",
            "Tensor(\"Relu_6:0\", shape=(?, 18, 1, 128), dtype=float32)\n",
            "2304\n",
            "INFO:tensorflow:Restoring parameters from drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/models/gan.ckpt-49999\n",
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TRV.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/DIS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TMO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TIF.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TWX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TJX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TMK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TSS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TSCO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TDG.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TRIP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/FOXA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/FOX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/TSN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/USB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UDR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ULTA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UAA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UNP.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UAL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UNH.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UPS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/URI.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UTX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UHS.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/UNM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VFC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VLO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VAR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VTR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VRSN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VRSK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VZ.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VRTX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VIAB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/V.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VNO.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/VMC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WMT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WBA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WAT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WEC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WFC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WELL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WDC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WU.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WRK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WY.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WHR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WMB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WLTW.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WYN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WYNN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XEL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XRX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XLNX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XYL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/YUM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZBH.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZION.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZTS.csv\n",
            "[0]\ttrain-mlogloss:0.68647\ttest-mlogloss:0.687511\n",
            "Multiple eval metrics have been passed: 'test-mlogloss' will be used for early stopping.\n",
            "\n",
            "Will train until test-mlogloss hasn't improved in 100 rounds.\n",
            "[1]\ttrain-mlogloss:0.680013\ttest-mlogloss:0.682069\n",
            "[2]\ttrain-mlogloss:0.673439\ttest-mlogloss:0.676391\n",
            "[3]\ttrain-mlogloss:0.667061\ttest-mlogloss:0.670867\n",
            "[4]\ttrain-mlogloss:0.660822\ttest-mlogloss:0.665521\n",
            "[5]\ttrain-mlogloss:0.654754\ttest-mlogloss:0.660371\n",
            "[6]\ttrain-mlogloss:0.648902\ttest-mlogloss:0.655522\n",
            "[7]\ttrain-mlogloss:0.643161\ttest-mlogloss:0.650734\n",
            "[8]\ttrain-mlogloss:0.637542\ttest-mlogloss:0.646072\n",
            "[9]\ttrain-mlogloss:0.631791\ttest-mlogloss:0.641142\n",
            "[10]\ttrain-mlogloss:0.626301\ttest-mlogloss:0.63661\n",
            "[11]\ttrain-mlogloss:0.621005\ttest-mlogloss:0.632231\n",
            "[12]\ttrain-mlogloss:0.615705\ttest-mlogloss:0.62782\n",
            "[13]\ttrain-mlogloss:0.610438\ttest-mlogloss:0.623493\n",
            "[14]\ttrain-mlogloss:0.605131\ttest-mlogloss:0.619007\n",
            "[15]\ttrain-mlogloss:0.600051\ttest-mlogloss:0.61476\n",
            "[16]\ttrain-mlogloss:0.595298\ttest-mlogloss:0.610928\n",
            "[17]\ttrain-mlogloss:0.590267\ttest-mlogloss:0.606741\n",
            "[18]\ttrain-mlogloss:0.585415\ttest-mlogloss:0.60266\n",
            "[19]\ttrain-mlogloss:0.580583\ttest-mlogloss:0.598631\n",
            "[20]\ttrain-mlogloss:0.575959\ttest-mlogloss:0.59484\n",
            "[21]\ttrain-mlogloss:0.571392\ttest-mlogloss:0.591064\n",
            "[22]\ttrain-mlogloss:0.566846\ttest-mlogloss:0.587367\n",
            "[23]\ttrain-mlogloss:0.562237\ttest-mlogloss:0.583497\n",
            "[24]\ttrain-mlogloss:0.557888\ttest-mlogloss:0.579965\n",
            "[25]\ttrain-mlogloss:0.55367\ttest-mlogloss:0.57659\n",
            "[26]\ttrain-mlogloss:0.54963\ttest-mlogloss:0.573427\n",
            "[27]\ttrain-mlogloss:0.545299\ttest-mlogloss:0.569765\n",
            "[28]\ttrain-mlogloss:0.541182\ttest-mlogloss:0.566441\n",
            "[29]\ttrain-mlogloss:0.537113\ttest-mlogloss:0.563137\n",
            "[30]\ttrain-mlogloss:0.533161\ttest-mlogloss:0.55994\n",
            "[31]\ttrain-mlogloss:0.529383\ttest-mlogloss:0.556963\n",
            "[32]\ttrain-mlogloss:0.525637\ttest-mlogloss:0.554037\n",
            "[33]\ttrain-mlogloss:0.52192\ttest-mlogloss:0.55109\n",
            "[34]\ttrain-mlogloss:0.518278\ttest-mlogloss:0.548221\n",
            "[35]\ttrain-mlogloss:0.514525\ttest-mlogloss:0.545203\n",
            "[36]\ttrain-mlogloss:0.510884\ttest-mlogloss:0.542296\n",
            "[37]\ttrain-mlogloss:0.507258\ttest-mlogloss:0.539417\n",
            "[38]\ttrain-mlogloss:0.503617\ttest-mlogloss:0.536467\n",
            "[39]\ttrain-mlogloss:0.500242\ttest-mlogloss:0.533859\n",
            "[40]\ttrain-mlogloss:0.496996\ttest-mlogloss:0.53138\n",
            "[41]\ttrain-mlogloss:0.493535\ttest-mlogloss:0.528562\n",
            "[42]\ttrain-mlogloss:0.490276\ttest-mlogloss:0.526012\n",
            "[43]\ttrain-mlogloss:0.487084\ttest-mlogloss:0.523506\n",
            "[44]\ttrain-mlogloss:0.483931\ttest-mlogloss:0.521054\n",
            "[45]\ttrain-mlogloss:0.480729\ttest-mlogloss:0.518484\n",
            "[46]\ttrain-mlogloss:0.477588\ttest-mlogloss:0.515976\n",
            "[47]\ttrain-mlogloss:0.47458\ttest-mlogloss:0.513644\n",
            "[48]\ttrain-mlogloss:0.471563\ttest-mlogloss:0.51127\n",
            "[49]\ttrain-mlogloss:0.468673\ttest-mlogloss:0.509086\n",
            "[50]\ttrain-mlogloss:0.465826\ttest-mlogloss:0.506932\n",
            "[51]\ttrain-mlogloss:0.462855\ttest-mlogloss:0.50458\n",
            "[52]\ttrain-mlogloss:0.460023\ttest-mlogloss:0.502415\n",
            "[53]\ttrain-mlogloss:0.457261\ttest-mlogloss:0.500299\n",
            "[54]\ttrain-mlogloss:0.454519\ttest-mlogloss:0.498254\n",
            "[55]\ttrain-mlogloss:0.451615\ttest-mlogloss:0.495956\n",
            "[56]\ttrain-mlogloss:0.448931\ttest-mlogloss:0.493918\n",
            "[57]\ttrain-mlogloss:0.446377\ttest-mlogloss:0.492067\n",
            "[58]\ttrain-mlogloss:0.443781\ttest-mlogloss:0.490085\n",
            "[59]\ttrain-mlogloss:0.441287\ttest-mlogloss:0.488203\n",
            "[60]\ttrain-mlogloss:0.438973\ttest-mlogloss:0.486524\n",
            "[61]\ttrain-mlogloss:0.436433\ttest-mlogloss:0.484647\n",
            "[62]\ttrain-mlogloss:0.433876\ttest-mlogloss:0.482691\n",
            "[63]\ttrain-mlogloss:0.431554\ttest-mlogloss:0.481015\n",
            "[64]\ttrain-mlogloss:0.429172\ttest-mlogloss:0.47922\n",
            "[65]\ttrain-mlogloss:0.426793\ttest-mlogloss:0.47743\n",
            "[66]\ttrain-mlogloss:0.424395\ttest-mlogloss:0.475569\n",
            "[67]\ttrain-mlogloss:0.422033\ttest-mlogloss:0.473781\n",
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            "[71]\ttrain-mlogloss:0.413081\ttest-mlogloss:0.467193\n",
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            "[75]\ttrain-mlogloss:0.404618\ttest-mlogloss:0.460999\n",
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            "[77]\ttrain-mlogloss:0.400602\ttest-mlogloss:0.458123\n",
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            "[80]\ttrain-mlogloss:0.394687\ttest-mlogloss:0.453858\n",
            "[81]\ttrain-mlogloss:0.392766\ttest-mlogloss:0.452484\n",
            "[82]\ttrain-mlogloss:0.390745\ttest-mlogloss:0.450952\n",
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            "[84]\ttrain-mlogloss:0.387074\ttest-mlogloss:0.448383\n",
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            "[86]\ttrain-mlogloss:0.383373\ttest-mlogloss:0.445764\n",
            "[87]\ttrain-mlogloss:0.381601\ttest-mlogloss:0.444532\n",
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            "[89]\ttrain-mlogloss:0.378195\ttest-mlogloss:0.442184\n",
            "[90]\ttrain-mlogloss:0.376499\ttest-mlogloss:0.441018\n",
            "[91]\ttrain-mlogloss:0.374842\ttest-mlogloss:0.439893\n",
            "[92]\ttrain-mlogloss:0.373289\ttest-mlogloss:0.438892\n",
            "[93]\ttrain-mlogloss:0.371654\ttest-mlogloss:0.437792\n",
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            "[95]\ttrain-mlogloss:0.368302\ttest-mlogloss:0.435403\n",
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            "[113]\ttrain-mlogloss:0.343829\ttest-mlogloss:0.420023\n",
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            "[158]\ttrain-mlogloss:0.299252\ttest-mlogloss:0.393835\n",
            "[159]\ttrain-mlogloss:0.298433\ttest-mlogloss:0.393335\n",
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            "[912]\ttrain-mlogloss:0.175348\ttest-mlogloss:0.347884\n",
            "[913]\ttrain-mlogloss:0.175255\ttest-mlogloss:0.347854\n",
            "[914]\ttrain-mlogloss:0.175192\ttest-mlogloss:0.347918\n",
            "[915]\ttrain-mlogloss:0.175128\ttest-mlogloss:0.347878\n",
            "[916]\ttrain-mlogloss:0.17508\ttest-mlogloss:0.347864\n",
            "[917]\ttrain-mlogloss:0.175026\ttest-mlogloss:0.347852\n",
            "[918]\ttrain-mlogloss:0.174967\ttest-mlogloss:0.347813\n",
            "[919]\ttrain-mlogloss:0.174898\ttest-mlogloss:0.347751\n",
            "[920]\ttrain-mlogloss:0.174848\ttest-mlogloss:0.347806\n",
            "[921]\ttrain-mlogloss:0.174806\ttest-mlogloss:0.347782\n",
            "[922]\ttrain-mlogloss:0.174747\ttest-mlogloss:0.347736\n",
            "[923]\ttrain-mlogloss:0.174685\ttest-mlogloss:0.347652\n",
            "[924]\ttrain-mlogloss:0.174635\ttest-mlogloss:0.347697\n",
            "[925]\ttrain-mlogloss:0.174581\ttest-mlogloss:0.347631\n",
            "[926]\ttrain-mlogloss:0.174535\ttest-mlogloss:0.347588\n",
            "[927]\ttrain-mlogloss:0.174446\ttest-mlogloss:0.347552\n",
            "[928]\ttrain-mlogloss:0.17439\ttest-mlogloss:0.347584\n",
            "[929]\ttrain-mlogloss:0.174334\ttest-mlogloss:0.347628\n",
            "[930]\ttrain-mlogloss:0.174241\ttest-mlogloss:0.347615\n",
            "[931]\ttrain-mlogloss:0.174196\ttest-mlogloss:0.347565\n",
            "[932]\ttrain-mlogloss:0.174147\ttest-mlogloss:0.347466\n",
            "[933]\ttrain-mlogloss:0.174099\ttest-mlogloss:0.347517\n",
            "[934]\ttrain-mlogloss:0.174063\ttest-mlogloss:0.34759\n",
            "[935]\ttrain-mlogloss:0.174027\ttest-mlogloss:0.347665\n",
            "[936]\ttrain-mlogloss:0.173978\ttest-mlogloss:0.347667\n",
            "[937]\ttrain-mlogloss:0.173905\ttest-mlogloss:0.34767\n",
            "[938]\ttrain-mlogloss:0.173854\ttest-mlogloss:0.347681\n",
            "[939]\ttrain-mlogloss:0.173786\ttest-mlogloss:0.34769\n",
            "[940]\ttrain-mlogloss:0.173748\ttest-mlogloss:0.347694\n",
            "[941]\ttrain-mlogloss:0.173709\ttest-mlogloss:0.347675\n",
            "[942]\ttrain-mlogloss:0.17365\ttest-mlogloss:0.347646\n",
            "[943]\ttrain-mlogloss:0.173587\ttest-mlogloss:0.347553\n",
            "[944]\ttrain-mlogloss:0.173474\ttest-mlogloss:0.347517\n",
            "[945]\ttrain-mlogloss:0.173397\ttest-mlogloss:0.347494\n",
            "[946]\ttrain-mlogloss:0.173363\ttest-mlogloss:0.347557\n",
            "[947]\ttrain-mlogloss:0.173323\ttest-mlogloss:0.347546\n",
            "[948]\ttrain-mlogloss:0.173265\ttest-mlogloss:0.347548\n",
            "[949]\ttrain-mlogloss:0.173182\ttest-mlogloss:0.347475\n",
            "[950]\ttrain-mlogloss:0.173131\ttest-mlogloss:0.347442\n",
            "[951]\ttrain-mlogloss:0.17306\ttest-mlogloss:0.347421\n",
            "[952]\ttrain-mlogloss:0.172994\ttest-mlogloss:0.347404\n",
            "[953]\ttrain-mlogloss:0.172944\ttest-mlogloss:0.347419\n",
            "[954]\ttrain-mlogloss:0.17289\ttest-mlogloss:0.347333\n",
            "[955]\ttrain-mlogloss:0.172851\ttest-mlogloss:0.347312\n",
            "[956]\ttrain-mlogloss:0.172794\ttest-mlogloss:0.34734\n",
            "[957]\ttrain-mlogloss:0.172701\ttest-mlogloss:0.347217\n",
            "[958]\ttrain-mlogloss:0.172616\ttest-mlogloss:0.347216\n",
            "[959]\ttrain-mlogloss:0.172561\ttest-mlogloss:0.347185\n",
            "[960]\ttrain-mlogloss:0.172484\ttest-mlogloss:0.347235\n",
            "[961]\ttrain-mlogloss:0.172423\ttest-mlogloss:0.347199\n",
            "[962]\ttrain-mlogloss:0.172377\ttest-mlogloss:0.347066\n",
            "[963]\ttrain-mlogloss:0.17232\ttest-mlogloss:0.346988\n",
            "[964]\ttrain-mlogloss:0.172271\ttest-mlogloss:0.346974\n",
            "[965]\ttrain-mlogloss:0.172219\ttest-mlogloss:0.346899\n",
            "[966]\ttrain-mlogloss:0.172173\ttest-mlogloss:0.346808\n",
            "[967]\ttrain-mlogloss:0.172053\ttest-mlogloss:0.346788\n",
            "[968]\ttrain-mlogloss:0.172006\ttest-mlogloss:0.346823\n",
            "[969]\ttrain-mlogloss:0.171945\ttest-mlogloss:0.346836\n",
            "[970]\ttrain-mlogloss:0.171884\ttest-mlogloss:0.346806\n",
            "[971]\ttrain-mlogloss:0.171836\ttest-mlogloss:0.346798\n",
            "[972]\ttrain-mlogloss:0.171785\ttest-mlogloss:0.346756\n",
            "[973]\ttrain-mlogloss:0.171728\ttest-mlogloss:0.346725\n",
            "[974]\ttrain-mlogloss:0.171661\ttest-mlogloss:0.346777\n",
            "[975]\ttrain-mlogloss:0.171593\ttest-mlogloss:0.346762\n",
            "[976]\ttrain-mlogloss:0.171543\ttest-mlogloss:0.34672\n",
            "[977]\ttrain-mlogloss:0.171481\ttest-mlogloss:0.346719\n",
            "[978]\ttrain-mlogloss:0.171441\ttest-mlogloss:0.346723\n",
            "[979]\ttrain-mlogloss:0.171383\ttest-mlogloss:0.346729\n",
            "[980]\ttrain-mlogloss:0.17134\ttest-mlogloss:0.346617\n",
            "[981]\ttrain-mlogloss:0.171277\ttest-mlogloss:0.346551\n",
            "[982]\ttrain-mlogloss:0.171222\ttest-mlogloss:0.346658\n",
            "[983]\ttrain-mlogloss:0.171154\ttest-mlogloss:0.346626\n",
            "[984]\ttrain-mlogloss:0.171092\ttest-mlogloss:0.346658\n",
            "[985]\ttrain-mlogloss:0.171043\ttest-mlogloss:0.346754\n",
            "[986]\ttrain-mlogloss:0.170984\ttest-mlogloss:0.346818\n",
            "[987]\ttrain-mlogloss:0.170939\ttest-mlogloss:0.346805\n",
            "[988]\ttrain-mlogloss:0.170881\ttest-mlogloss:0.346781\n",
            "[989]\ttrain-mlogloss:0.170824\ttest-mlogloss:0.346771\n",
            "[990]\ttrain-mlogloss:0.170788\ttest-mlogloss:0.346707\n",
            "[991]\ttrain-mlogloss:0.170736\ttest-mlogloss:0.346787\n",
            "[992]\ttrain-mlogloss:0.170695\ttest-mlogloss:0.346717\n",
            "[993]\ttrain-mlogloss:0.170641\ttest-mlogloss:0.346676\n",
            "[994]\ttrain-mlogloss:0.170595\ttest-mlogloss:0.346654\n",
            "[995]\ttrain-mlogloss:0.170531\ttest-mlogloss:0.346578\n",
            "[996]\ttrain-mlogloss:0.170469\ttest-mlogloss:0.346529\n",
            "[997]\ttrain-mlogloss:0.170429\ttest-mlogloss:0.346536\n",
            "[998]\ttrain-mlogloss:0.170371\ttest-mlogloss:0.346617\n",
            "[999]\ttrain-mlogloss:0.170312\ttest-mlogloss:0.346695\n",
            "[[142785    533]\n",
            " [ 25904   4311]]\n",
            "Normalized confusion matrix\n",
            "[[0.996281   0.003719  ]\n",
            " [0.85732252 0.14267748]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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V2v/KlyEWiuThkBGqEEIIy6jeN0qShCqEEMIy5NaDQgghhKg0GaEKIYSwiOo+QpWEKoQQ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\niAGUUEVERAzw/5Nm5SrTsJY5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1b6b7afef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3lPwUmniYB2k",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Change the names of the files in the deployed_model folder\n",
        "\n",
        "Directions: Run after training and placing the GAN checkpoints you desire into the deployed_models folder. Then thisd code will rename all of them so that they run for the \"Get the Predictions\" section."
      ]
    },
    {
      "metadata": {
        "id": "fzFuIWXtYCOx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Below Code Gets Rid of Copy Of when making a copy of the models.\n",
        "for filename in os.listdir(f'{googlepath}deployed_model/'):\n",
        "    split = filename.split(\" \")\n",
        "    if len(split) == 3:\n",
        "        os.rename(f'{googlepath}deployed_model/{filename}', f'{googlepath}deployed_model/{split[2]}')\n",
        "    else:\n",
        "        print(\"Nothing to rename.\")\n",
        "\n",
        "# Below Code Gets Rid of Checkpoint Number in Name\n",
        "for filename in os.listdir(f'{googlepath}deployed_model/'):\n",
        "    split = filename.split(\".\")\n",
        "    if len(split) == 3:\n",
        "        prefix, _, suffix = split\n",
        "        new_name = prefix + \".\" + suffix\n",
        "        os.rename(f'{googlepath}deployed_model/{filename}', f'{googlepath}deployed_model/{new_name}')\n",
        "    elif filename == \"clf.pkl\":\n",
        "        os.rename(f'{googlepath}deployed_model/{filename}', f'{googlepath}deployed_model/xgb')\n",
        "    else:\n",
        "        print(\"Nothing to rename.\")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vXGRuO7zw7JI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Get the Predictions"
      ]
    },
    {
      "metadata": {
        "id": "Qmv5jskCw54L",
        "colab_type": "code",
        "outputId": "b321798c-ef50-4514-f16b-c09fa5d53ab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 9159
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "# from gan import GAN\n",
        "import random\n",
        "import tensorflow as tf\n",
        "import xgboost as xgb\n",
        "from sklearn.externals import joblib\n",
        "\n",
        "\n",
        "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"\n",
        "\n",
        "class Predict:\n",
        "\n",
        "    def __init__(self, num_historical_days=20, days=10, pct_change=0, \n",
        "                 gan_model=f'{googlepath}deployed_model/gan', \n",
        "                 cnn_model=f'{googlepath}deployed_model/cnn', \n",
        "                 xgb_model=f'{googlepath}deployed_model/xgb'):\n",
        "        self.data = []\n",
        "        self.num_historical_days = num_historical_days\n",
        "        self.gan_model = gan_model\n",
        "        self.cnn_model = cnn_model\n",
        "        self.xgb_model = xgb_model\n",
        "        # assert os.path.exists(gan_model)\n",
        "        # assert os.path.exists(cnn_modle)\n",
        "        # assert os.path.exists(xgb_model)\n",
        "\n",
        "        files = [os.path.join(f'{googlepath}stock_data', f) for f in os.listdir(f'{googlepath}stock_data')]\n",
        "        for file in files:\n",
        "            print(file)\n",
        "            df = pd.read_csv(file, index_col='timestamp', parse_dates=True)\n",
        "            df = df.sort_index(ascending=False)\n",
        "            df = df[['open', 'high', 'low', 'close', 'volume']]\n",
        "            df = ((df -\n",
        "                  df.rolling(num_historical_days).mean().shift(-num_historical_days)) /\n",
        "                  (df.rolling(num_historical_days).max().shift(-num_historical_days) -\n",
        "                  df.rolling(num_historical_days).min().shift(-num_historical_days)))\n",
        "            df = df.dropna()\n",
        "\n",
        "            \"\"\"\n",
        "            file.split --> is the symbol of the current file. Append a tuple of\n",
        "            that symbol and the dataframe index[0] which is the timestamp, and\n",
        "            thirdly append the data for 200 to 200 + num_historical_days values\n",
        "            (open, high, low, close, volume). For each symbol we have, we are\n",
        "            predicting based on the df[200:200+num_historical_days].values...\n",
        "            \"\"\"\n",
        "            self.data.append((file.split('/')[-1], df.index[0], df[200:200+num_historical_days].values))\n",
        "            \n",
        "#         print(\"Data: \", self.data)\n",
        "\n",
        "\n",
        "    def gan_predict(self):\n",
        "        # clears the default graph stack and resets the global default graph.\n",
        "\n",
        "        tf.reset_default_graph() \n",
        "        gan = GAN(num_features=5, num_historical_days=self.num_historical_days,\n",
        "                  generator_input_size=200, is_train=False)\n",
        "        # A class for running Tensorflow operations. A session object\n",
        "        # encapsulates the environment in which Operation objects are executed,\n",
        "        # and Tensor objects are evaluated. A session may own resources, such as\n",
        "        # tf.Variable, tf.QueueBase and tf.ReaderBase. It is important to\n",
        "        # release these resources when they are no longer required. Invoke\n",
        "        # tf.Session.close method on the session or use the session as a context\n",
        "        # manager. \n",
        "        # with tf.Session() as sess:\n",
        "        #   sess.run(...)\n",
        "        # or\n",
        "        # sess = tf.Session()\n",
        "        # sess.run(...)\n",
        "        # sess.close()     \n",
        "        with tf.Session() as sess:\n",
        "            sess.run(tf.global_variables_initializer())\n",
        "            saver = tf.train.Saver()\n",
        "            saver.restore(sess, self.gan_model)\n",
        "            # Reconstruct a Python object from a file persisted with joblib.dump\n",
        "            clf = joblib.load(self.xgb_model)\n",
        "            for sym, date, data in self.data:\n",
        "                # run takes in feed_dict=None, session=None. A feed_dict is a\n",
        "                # dictionary that maps Tensor objects to feed values. In this\n",
        "                # case, I believe we are doing run( fetches, feed_dict=None...)\n",
        "                # case where the fetches is gan.features and the feed_dict\n",
        "                # points to the gan.X dictionary which points to data. The\n",
        "                # fetches argument may be a single graph element, or an\n",
        "                # arbitrarily nested list, tuple, namedtuple, dict, or\n",
        "                # OrderedDict containing graph elements at its leaves.\n",
        "#                 print(\"data: \", data)\n",
        "#                 print(\"data length: \", len(data))\n",
        "#                 print(\"gan features: \", gan.features)\n",
        "                features = sess.run(gan.features, feed_dict={gan.X:[data]})\n",
        "                # Value returned by run() has the same shape as the fetches\n",
        "                # argument, where the leaves are replaced by the corresponding\n",
        "                # values returned by TensorFlow.  \n",
        "                \n",
        "                # xgb.DMatrix, construct one from either a dense matrix, a\n",
        "                # sparse matrix, or a local file. Supported input file formats\n",
        "                # are either a libsvm text file or a binary file that was\n",
        "                # created previously by xgb.DMatrix.save. Internal data\n",
        "                # structure that is used by XGBoost which is optimized for both\n",
        "                # memory efficiency and training speed.\n",
        "                features = xgb.DMatrix(features)\n",
        "                \n",
        "\n",
        "                # The clf predict is the xgb classifier that is used on the gan\n",
        "                # features (the flattened last layer of the convolutional neural\n",
        "                # network, that is the discriminator). As far as I can tell, we\n",
        "                # are using the GAN on the past 20 days to come up with some\n",
        "                # features. Then these features are plugged into the XGBoost\n",
        "                # Classifier. Then the XGBoost Classifier makes a prediction for\n",
        "                # the stock (going Up or Down).\n",
        "                print('{} {} {}'.format(str(date).split(' ')[0], sym, clf.predict(features)[0][1] > 0.5))\n",
        "\n",
        "\n",
        "# if __name__ == '__main__':\n",
        "p = Predict(num_historical_days=HISTORICAL_DAYS_AMOUNT, days=DAYS_AHEAD, pct_change=PCT_CHANGE_AMOUNT)\n",
        "p.gan_predict()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WBA.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WAT.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WEC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WFC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WELL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WDC.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WU.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WRK.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WY.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WHR.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WMB.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WLTW.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WYN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/WYNN.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XEL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XRX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XLNX.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/XYL.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/YUM.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZBH.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZION.csv\n",
            "drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/stock_data/ZTS.csv\n",
            "Tensor(\"Relu:0\", shape=(?, 20, 1, 32), dtype=float32)\n",
            "Tensor(\"Relu_1:0\", shape=(?, 20, 1, 64), dtype=float32)\n",
            "Tensor(\"Relu_2:0\", shape=(?, 18, 1, 128), dtype=float32)\n",
            "2304\n",
            "Tensor(\"Relu_4:0\", shape=(?, 20, 1, 32), dtype=float32)\n",
            "Tensor(\"Relu_5:0\", shape=(?, 20, 1, 64), dtype=float32)\n",
            "Tensor(\"Relu_6:0\", shape=(?, 18, 1, 128), dtype=float32)\n",
            "2304\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-21b86a6800a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[0;31m# if __name__ == '__main__':\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_historical_days\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mHISTORICAL_DAYS_AMOUNT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdays\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDAYS_AHEAD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpct_change\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPCT_CHANGE_AMOUNT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgan_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-12-21b86a6800a0>\u001b[0m in \u001b[0;36mgan_predict\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_variables_initializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgan_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m             \u001b[0;31m# Reconstruct a Python object from a file persisted with joblib.dump\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m             \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjoblib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxgb_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, sess, save_path)\u001b[0m\n\u001b[1;32m   1536\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheckpoint_management\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckpoint_exists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1537\u001b[0m       raise ValueError(\"The passed save_path is not a valid checkpoint: \"\n\u001b[0;32m-> 1538\u001b[0;31m                        + compat.as_text(save_path))\n\u001b[0m\u001b[1;32m   1539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m     \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Restoring parameters from %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The passed save_path is not a valid checkpoint: drive/My Drive/Colab Notebooks/SeniorDesignGAN/MiloGAN/deployed_model/gan"
          ]
        }
      ]
    }
  ]
}